[
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    "content": "# ---> Python\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n"
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    "path": "README.md",
    "content": "# SpyTorch\nA tutorial on surrogate gradient learning in spiking neural networks\n\nVersion: 0.4\n\n[![DOI](https://zenodo.org/badge/170391179.svg)](https://zenodo.org/badge/latestdoi/170391179)\n\nThis repository contains tutorial files to get you started with the basic ideas\nof surrogate gradient learning in spiking neural networks using PyTorch. \n\nYou find a brief introductory video accompanying these notebooks here https://youtu.be/xPYiAjceAqU\n\nFeedback and contributions are welcome.\n\nFor more information on surrogate gradient learning please refer to:\n> Neftci, E.O., Mostafa, H., and Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks. IEEE Signal Processing Magazine 36, 51–63.\n> https://ieeexplore.ieee.org/document/8891809\n> preprint: https://arxiv.org/abs/1901.09948\n\n\nAlso see https://github.com/surrogate-gradient-learning\n\n## Copyright and license\n\nCopyright 2019-2022 Friedemann Zenke, https://fzenke.net\n\nThis work is licensed under a Creative Commons Attribution 4.0 International License.\nhttp://creativecommons.org/licenses/by/4.0/\n"
  },
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    "path": "notebooks/.gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n"
  },
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    "path": "notebooks/SpyTorchTutorial1.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tutorial 1: Training a spiking neural network with surrogate gradients\\n\",\n    \"\\n\",\n    \"Friedemann Zenke (https://fzenke.net)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> For more details on surrogate gradient learning, please see: \\n\",\n    \"> Neftci, E.O., Mostafa, H., and Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks.\\n\",\n    \"> https://arxiv.org/abs/1901.09948\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Introduction \\n\",\n    \"\\n\",\n    \"The last months have seen a surge of interest in training spiking neural networks to do meaningful computations. On the one hand, this surge was fueled by the limited accomplishment of more traditional, and often considered more biologically plausible, learning paradigms in creating functional neural networks that solve interesting computational problems. This limitation was met by the undeniable success of deep neural networks in acing a diversity of challenging computational problems. A success that has raised both the bar and the question of how well this progress would translate to spiking neural networks.\\n\",\n    \"\\n\",\n    \"The rise of deep learning over the last decade is in large part due to GPUs and their increased computational power, growing training data sets, and --- perhaps most importantly --- advances in understanding the quirks and needs of the error back-propagation algorithm. For instance, we now know that we have to avoid vanishing and exploding gradients, a feat that can be accomplished by choice of a sensible nonlinearity, proper weight initialization, and a suitable optimizer. Powerful software packages supporting auto-differentiation have since made mangling with deep neural networks a breeze in comparison to what it used to be. This development begs the question of how much of this knowledge gain from deep learning and its tools we can leverage to train spiking neural networks. Although a complete answer to these questions cannot be given at the moment, it seems that we can learn a lot.\\n\",\n    \"\\n\",\n    \"In this tutorial, we use insights and tools from machine learning to build, step-by-step, a spiking neural network. Explicitly, we set out with the goal of building networks that solve (simple) real-world problems. To that end, we focus on classification problems and use supervised learning in conjunction with the aforementioned back-propagation algorithm. To do this, we have to overcome a vanishing gradient problem caused by the binary nature of the spikes themselves.\\n\",\n    \"\\n\",\n    \"In this tutorial, we will first show how a simple feed-forward spiking neural network of leaky integrate-and-fire (LIF) neurons with current-based synapses can be formally mapped to a discrete-time recurrent neural network (RNN). We will use this formulation to explain why gradients vanish at spikes and show one way of how the problem can be alleviated. Specifically, we will introduce surrogate gradients and provide practical examples of how they can be implemented in PyTorch.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Mapping LIF neurons to RNN dynamics\\n\",\n    \"\\n\",\n    \"The de-facto standard neuron model for network simulations in computational neuroscience is the LIF neuron model which is often formally written as a time continuous dynamical system in differential form:\\n\",\n    \"$$\\\\tau_\\\\mathrm{mem} \\\\frac{\\\\mathrm{d}U_i^{(l)}}{\\\\mathrm{d}t} = -(U_i^{(l)}-U_\\\\mathrm{rest}) + RI_i^{(l)}$$\\n\",\n    \"where $U_i$ is the membrane potential of neuron $i$ in layer $l$, $U_\\\\mathrm{rest}$ is the resting potential, $\\\\tau_\\\\mathrm{mem}$ is the membrane time constant, $R$ is the input resistance, and $I_i$ is the input current. The membrane potential $U_i$ characterizes the hidden state of each neuron and, importantly, it is not directly communicated to downstream neurons. However, a neuron fires an action potential or spike at the time $t$ when its membrane voltage exceeds the firing threshold $\\\\vartheta$. After having fired a spike, a neurons membrane voltage is reset $U_i \\\\rightarrow U_\\\\mathrm{rest}$. We write\\n\",\n    \"$$S_i^{(l)}(t)=\\\\sum_{k \\\\in C_i^l} \\\\delta(t-t_j^k)$$ \\n\",\n    \"for the spike train (ie. the sum of all spikes $C_i^l$ emitted by neuron $i$ in layer $l$). Here $\\\\delta$ is the Dirac delta function and $t_i^k$ are the associated firing times of the neuron.\\n\",\n    \"\\n\",\n    \"Spikes travel down the axon and generate a postsynaptic currents in connected neurons. Using our above formalism we can thus write\\n\",\n    \"$$\\\\frac{\\\\mathrm{d}I_i}{\\\\mathrm{d}t}= -\\\\frac{I_i(t)}{\\\\tau_\\\\mathrm{syn}} + \\\\sum_j W_{ij} S_j^{(0)}(t) + \\\\sum_j V_{ij} S_j^{(1)}(t)$$\\n\",\n    \"where we have introduced the synaptic weight matrices $W_{ij}$ (feed-forward), $V_{ij}$ (recurrent), and the synaptic decay time constant $\\\\tau_\\\\mathrm{syn}$.\\n\",\n    \"\\n\",\n    \"To link to RNNs apparent, we will now express the above equations in discrete time. In the interest of brevity we switch to natural units $U_\\\\mathrm{rest}=0$, $R=1$, and $\\\\vartheta=1$. Our arguments remain unaffected by this choice, and all results can always be re-scaled back to physical units. To highlight the nonlinear character of a spike, we start by noting that we can set\\n\",\n    \"$$S_i^{(l)}(t)=\\\\Theta(U_i^{(l)}(t)-\\\\vartheta)$$\\n\",\n    \"where $\\\\Theta$ denotes the Heaviside step function.\\n\",\n    \"\\n\",\n    \"Assuming a small simulation time step of $\\\\Delta_t>0$ we can approximate the synaptic dynamics by\\n\",\n    \"$$I_i^{(l)}(t+1) = \\\\alpha I_i^{(l)}(t) + \\\\sum_j W_{ij} S_j^{(l-1)}(t) +\\\\sum_j V_{ij} S_j^{(l)}(t)$$\\n\",\n    \"with the constant $\\\\alpha=\\\\exp\\\\left(-\\\\frac{\\\\Delta_t}{\\\\tau_\\\\mathrm{syn}} \\\\right)$. Further, the membrane dynamics can be written as\\n\",\n    \"$$U_i^{(l)}(t+1) = \\\\underbrace{\\\\beta U_i^{(l)}(t)}_{\\\\mathrm{leak}} + \\\\underbrace{I_i^{(l)}(t)}_{\\\\mathrm{input}} -\\\\underbrace{S_i^{(l)}(t)}_{\\\\mathrm{reset}}$$\\n\",\n    \"with the output $S_i(t) = \\\\Theta(U_i(t)-1)$ and the constant $\\\\beta=\\\\exp\\\\left(-\\\\frac{\\\\Delta_t}{\\\\tau_\\\\mathrm{mem}}\\\\right)$. Note the distinct terms on the right-hand-side of the equation which are responsible individually for i) leak, ii) synaptic input, and iii) the spike reset.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"These equations can be summarized succinctly as the computational graph of an RNN with a specific connectivity structure. \\n\",\n    \"<img src=\\\"figures/snn_graph/snn_graph.png\\\" width=\\\"450\\\">\\n\",\n    \"Time flows from left to right. Inputs enter the network at each time step from the bottom of the graph ($S_i^{(0)}$). These inputs sequentially influence the synaptic currents $I_i^{(1)}$, membrane potentials the $U_i^{(1)}$, and finally the spiking output $S_i^{(1)}$.  Moreover, dynamic quantities have direct input on future time steps. We have suppressed the indices $i$ in the figure for clarity.\\n\",\n    \"\\n\",\n    \"The computational graph illustrates a concept which is known as unrolling in time, which emphasizes the duality between a deep neural network and a recurrent neural network, which is nothing more but a deep network in time (with tied weights). Due to this fact, we can train RNNs using the back-propagation of error through time (BPTT). We will discuss problems arising from the binary character of the spiking nonlinearity later. For now, let us start by implementing the above dynamics in a three-layer spiking neural network in PyTorch.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example network\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's start with a simple multilayer network model with a single hidden layer, as shown below. For simplicity, we will not use recurrent connections $V$ for now, keeping in mind that they can be added later should the need arise.\\n\",\n    \"\\n\",\n    \"<img src=\\\"figures/mlp_sketch/mlp_sketch.png\\\">\\n\",\n    \"\\n\",\n    \"For the sake of argument, we set the numbers for the input, hidden and output neurons as follows:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"nb_inputs  = 100\\n\",\n    \"nb_hidden  = 4\\n\",\n    \"nb_outputs = 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As we have seen above, we are technically simulating an RNN. Thus we have to simulate our neurons for a certain number of timesteps. We will use 1ms timesteps, and we want to simulate our network for say 200 timesteps. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"time_step = 1e-3\\n\",\n    \"nb_steps  = 200\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To take advantage of parallelism, we will set up our code to work on batches of data like this is usually done for neural networks that are trained in a supervised manner.\\n\",\n    \"To that end, we specify a batch size here.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"batch_size = 256\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With these basic design choices made, we can now start building the actual network. Here we will be using PyTorch, but you will be able to reproduce these results in most common machine learning libraries.\\n\",\n    \"\\n\",\n    \"We start by importing the libraries we need.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib.gridspec import GridSpec\\n\",\n    \"import seaborn as sns\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dtype = torch.float\\n\",\n    \"device = torch.device(\\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"# Uncomment the line below to run on GPU\\n\",\n    \"# device = torch.device(\\\"cuda:0\\\") \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### A simple synthetic dataset \\n\",\n    \"\\n\",\n    \"We start by generating some random spiking data set, which we will use as input to our network. In the beginning, we will work with a single batch of data. It will be straight forward to expand later what we have learned to larger datasets.\\n\",\n    \"\\n\",\n    \"Suppose we want our network to classify a set of different sparse input spike trains into two categories. \\n\",\n    \"\\n\",\n    \"To generate some synthetic data, we fill a tensor of (batch_size x nb_steps x nb_inputs) with random uniform numbers between 0 and 1 and use this to generate our input dataset:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"freq = 5 # Hz\\n\",\n    \"prob = freq*time_step\\n\",\n    \"mask = torch.rand((batch_size,nb_steps,nb_inputs), device=device, dtype=dtype)\\n\",\n    \"x_data = torch.zeros((batch_size,nb_steps,nb_inputs), device=device, dtype=dtype, requires_grad=False)\\n\",\n    \"x_data[mask<prob] = 1.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If the plot the spike raster of the first input pattern, this synthetic dataset looks as follows.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"data_id = 0\\n\",\n    \"plt.imshow(x_data[data_id].cpu().t(), cmap=plt.cm.gray_r, aspect=\\\"auto\\\")\\n\",\n    \"plt.xlabel(\\\"Time (ms)\\\")\\n\",\n    \"plt.ylabel(\\\"Unit\\\")\\n\",\n    \"sns.despine()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, we assign a random label of 0 or 1 to each of our input patterns. Our network's task will be to differentiate these patterns.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y_data = torch.tensor(1*(np.random.rand(batch_size)<0.5), device=device)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that there is no structure in the data (because it is entirely random). Thus we won't worry about generalization now and only care about our ability to overfit these data with the spiking neural network we are going to build in a jiffy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setup of the spiking network model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now is the time to implement our LIF neuron model in discrete time.\\n\",\n    \"We will first do this step by step before we wrap all the steps into a function later on.\\n\",\n    \"But first, we fix several model constants such as the membrane and the synaptic time constant. Moreover, we define some essential variables, including our $\\\\alpha$ and $\\\\beta$ as described above. We do this now because we will use some of these variables to scale our weights to meaningful ranges.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tau_mem = 10e-3\\n\",\n    \"tau_syn = 5e-3\\n\",\n    \"\\n\",\n    \"alpha   = float(np.exp(-time_step/tau_syn))\\n\",\n    \"beta    = float(np.exp(-time_step/tau_mem))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we set up our weight matrices, which connect the input and the hidden layer, as well as the matrix connecting the hidden layer with the output layer. Moreover, we initialize these weights randomly from a normal distribution. Note that we scale the variance with the inverse square root of the number of input connections. Moreover, for the sake of simplicity, we ignore Dale's law in this tutorial. Thus weights can be either excitatory or inhibitory. This choice is prevalent in artificial neural networks.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"init done\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"weight_scale = 7*(1.0-beta) # this should give us some spikes to begin with\\n\",\n    \"\\n\",\n    \"w1 = torch.empty((nb_inputs, nb_hidden),  device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"torch.nn.init.normal_(w1, mean=0.0, std=weight_scale/np.sqrt(nb_inputs))\\n\",\n    \"\\n\",\n    \"w2 = torch.empty((nb_hidden, nb_outputs), device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"torch.nn.init.normal_(w2, mean=0.0, std=weight_scale/np.sqrt(nb_hidden))\\n\",\n    \"\\n\",\n    \"print(\\\"init done\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### A spiking neuron model in discrete time\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The first thing we need to do to implement our spiking neuron is to multiply all input spikes with the weight matrix. We have to do this for each time step in each input example in the batch. Because we have stored our input spikes in a rank three tensor we can express this operation in a single line:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"h1 = torch.einsum(\\\"abc,cd->abd\\\", (x_data, w1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"These \\\"weighted\\\" input spikes will now feed into our synaptic variable and, ultimately, the membrane potential. To trigger a spike, we need to define moreover a threshold or spike function, which we do in the following. We will later have to alter this definition to train the network, but more about that later.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### The spiking nonlinearity (the naive way)\\n\",\n    \"\\n\",\n    \"In discrete-time, as explained earlier, we can formulate our spiking nonlinearity as a Heaviside step function. So let's begin with defining a Heaviside function. One way of implementing it is the following:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def spike_fn(x):\\n\",\n    \"    out = torch.zeros_like(x)\\n\",\n    \"    out[x > 0] = 1.0\\n\",\n    \"    return out\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For each trial, we initialize the synaptic currents and membrane potentials at zero.\\n\",\n    \"Next, we need to implement a loop that simulates our neuron models over time. \\n\",\n    \"Moreover, we will record the membrane potentials and output spikes of all trials and all neurons.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"syn = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\\n\",\n    \"mem = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\\n\",\n    \"\\n\",\n    \"# Here we define two lists which we use to record the membrane potentials and output spikes\\n\",\n    \"mem_rec = []\\n\",\n    \"spk_rec = []\\n\",\n    \"\\n\",\n    \"# Here we loop over time\\n\",\n    \"for t in range(nb_steps):\\n\",\n    \"    mthr = mem-1.0\\n\",\n    \"    out = spike_fn(mthr)\\n\",\n    \"    rst = out.detach() # We do not want to backprop through the reset\\n\",\n    \"\\n\",\n    \"    new_syn = alpha*syn +h1[:,t]\\n\",\n    \"    new_mem = (beta*mem +syn)*(1.0-rst)\\n\",\n    \"    \\n\",\n    \"    mem_rec.append(mem)\\n\",\n    \"    spk_rec.append(out)\\n\",\n    \"    \\n\",\n    \"    mem = new_mem\\n\",\n    \"    syn = new_syn\\n\",\n    \"\\n\",\n    \"# Now we merge the recorded membrane potentials into a single tensor\\n\",\n    \"mem_rec = torch.stack(mem_rec,dim=1)\\n\",\n    \"spk_rec = torch.stack(spk_rec,dim=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And that's it. The above loop has now simulated our neurons for '''nb_steps''' and stored their membrane traces and output spikes. Let us take a look at those membrane potentials in which we directly \\\"paste\\\" the spikes for visual inspection. We will directly plot multiple trials at once and define a little helper function for this purpose.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_voltage_traces(mem, spk=None, dim=(3,5), spike_height=5):\\n\",\n    \"    gs=GridSpec(*dim)\\n\",\n    \"    if spk is not None:\\n\",\n    \"        dat = 1.0*mem\\n\",\n    \"        dat[spk>0.0] = spike_height\\n\",\n    \"        dat = dat.detach().cpu().numpy()\\n\",\n    \"    else:\\n\",\n    \"        dat = mem.detach().cpu().numpy()\\n\",\n    \"    for i in range(np.prod(dim)):\\n\",\n    \"        if i==0: a0=ax=plt.subplot(gs[i])\\n\",\n    \"        else: ax=plt.subplot(gs[i],sharey=a0)\\n\",\n    \"        ax.plot(dat[i])\\n\",\n    \"        ax.axis(\\\"off\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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AjbX+optrC5vKO8EROHDnHYrEAKDDVl+dbBvTw5flu9uX5ardPsmEpV5OuBZge7UmlaqGzvBEAVa1TGacBA1Jbh76JhAzvSwZ16eqjOh258xS3xulxnuj0OB/camTvtkrd5XWDAfrJy+kpBTCmLr0cSJJU1d8+HD4HONyX5yv35fmKfHk+hfoFEbts95pt80+EQ+YTgN9vd/RVo8kfy+ZSsxJJUyMVPXMLxn6sABcYkgMfAUgSMvUz1lN3mHjDc3zc/ofa9hiby2vY0ck7wubyXtuKortfNT1HP3lF3fFyNekNYNQxNU8PHFDz+vXAfJvLm7lUaXtTQGmpLlPa8F1qKpXmKjKiUTKj9QtBM6JCKZhZoXLJL/XHIwZecS72L73yB+V1gDbFIKtiFlhhIQz8Ejv3mhsNp+/2WziwtOu2TuWx96Pk/aJw5Q/KrafMi24C5l/wRvfrqW/HiwP5uQpCKAI7HYzs6Df55/uPGf3qfRet6Or6+h+by3vaf2VIc8A6Wvu3CjFQvA64TzvWg+0F/y7RVOJHUT/ofHiK5Vbn1UYvt5k+xyGtDiHa/Lc2l/ekPU2/4YSycISYh1A5UF1swvlVMtd5oxgj9c6DKhKypZAjU3+YtOKx7I6B/FwlkJ/7ECh3Gq1z686TDFXdnR5nM8m0Jd+YvnBVapcnmxuS1n+LcA6L3a9uNPxLagoVshw/Nb/P6XE+DTwHEA213lKz6aTpcb+PYPsZl4n6yjMnlpUdPGtnhNe4A7itr2v8LzaXd8BuvafGo7u2XYKYwcZ7bL6rbb9GmARaNStT7/DbHcf67Y56Zwu3NRW39bCtHKrcVslvd/S6bt7EzztHN1CipkWXK60jABFVDgEX4g6efUeL5rFZLCvMpntGvuV4dttRqM3l7WRzef+1ub79TFWlFwCqZPmMIlmej9sa37HGE7PJLUaoFiNAK8Bmc3m7Ar/NVzpPmpMkVlmoyFNtLu/Acn9+a5vL21irCwBwepzdnB7nY/nZmResMBn5Shm2rlDNujWqSuo6NZvnImevUlXhL6FEeKy6yDwSQAnLpnCV8dfa25vtcmmI0+M8HeFI4gb+6v1ev242l/eGo2v+75v4876z3EP7jD+MALeWlPb4bu2Gz30rV1+9TXIxydDd6XG+4vQ4xzs9zp2p2R4ELIh28C/QcnNyxidG67zTAOTSbr9OfG3a1X67o2ryxNtn3zR1gUlWVIAOad0e6iJb1keBrjaXt/WunrEBGKZtL0SolsuATOCw3blYswW/OtowhWSplqgqrfgt2i/4t+JgZM0TG3EHv1mptrkYocLuALx4Qu1T7U9IPVc6p32zyAM5ognlBct5b8NGzisr54XCzbyztsKbqihbzp6mYImI1SjvHp3BlcfcP1W79VahHGZ1lbjiFoPptisM+bFjlUmMd3qcb2gDs7ucHmf63r+mRqXd8XMVZBVqtaFQ7nQsGZUqZlV96EfzHcM+Nj1KK4r+xW1tPcH8kH2c8U2aUbatqTGe2G8Bbdv7eMOc8dcZv+l0q/HzgY8a334Ht/Ul3NadtaNLECavJQhTEpKx5BjAFnfOudtfVo/f7sjw2x3/57c7XvfbHck2l1dG9P2/x/K21HLRqhSppk77+qX5QRfCfJTE1k57u0VDdlrDgPba/q2b5mU822KTTItClTZFUe6/2ACSRLlB5ox2bQA6ZEajK1q80eO9fy3mv9N6GB+S5AgoRhTFXC4bq9KBy9O6Pn0CUKfuU1X5TKfHafBBz+piU+bq+dZoSbk5MuswyTJ0s8KRVSE+7ZSMMQopNdzmbw9IEuHgYa3CxUPzLS1+iM/ztrZVO0LFXQbEhss7GrhsJcxt0sZj56tdZ2kON9cBFwA1wGuB/Nx79uJd7hGa8IvNSv715flCTo/zZYQtXfHl+VYB+PJ8NU6P88HsMvXdce9FHwIoS2aN3+7o7Dh//XDgC0THE8Vt/RUoCRWbjkJWc1JWhORCMvim//B/no+cOeM0w983b1Cb/f6H0tcXAFaaTa0AMqLRaJnBYFhnMt6SFY12cHqcl/pWrjYAt99sOLvP89GzuiV3eLubJNWPGWYlWUwnVVX/hNtagFC5jsUdXK79XDfCxh2sxm2dgbCVn27K/DtbMpbJyeGl/K4J5erVV14NxDq15TaXt1tjLM/RtCyzgIxJVjMbzc2oqGj5eufHljx34z33/exVjpihIneJYFhyh2kCFRuSjlVqZSRZZKW23JgcKjFNNLutp+IORv12hwQ0A0ocBX5Fu0cH6gdUAC1RjDMkYzDLpmzYKj/FskxRcgkgcUJlVazOPo7bOgv4GXdQpV4InBh3aXNgKzWb3+44n/o6fjlQAqzKqS5zZFcbKEpOK/d45vxE/Xu2H7NAtadXKTxzljw0aqwemtLphSiK5SXJUFOztd9kw7L2wS5DXjB1G/hmJBef2nl6ID83YnN5pyAcSI9FlNFW2FzeRxFmnfsD+blbDKlLz5QMFRxRKyYFBkl9/LLwnQaEueRkm8tbCzwal8RFxrRFFyW1mkRU60MNqqoGDfLILuFI5j1bStrVYLYkS8GnhsxLb37CXHUFkPzI+TLRrCSK11sfs7m83snbCOU/nNIqVZY6FmapdStOtGZyFfVq2HZsbccEt3UoYMEd/HUvX+M+Y6lVOxyxRGT20QsMXPpzlM4b4fpvFV4429S8k7yO7qzjd/l/o4Heg+QlwwbJSwiSNkR0lzskNlOeixCksYkHNxgngai7NyAG6rdofkcnAG0QJpSYoHwHrR4Yktf13+Yeo50e572+PN92fYRok+qXIB0HsDE1K4LQHAGoBqL/Pmh8/xeTpCzY6kI5fNdxHe5w/xk+wmfOnDV5T+t/Q6qvT1GAckn62P9pm3erNtf7SvVYB31XbP3MqYqilBoM8r8W8+XAG5IcSQY4qrIWQzQ5Zo97Cq0DUcKZmwEkSUkDOlRstFyy+tdslEKzwVqF5ZxpKjdPUjjiZzPPvhnlqXeiPPRRlBPnqKiKQY2U9wYMhEu3KpPubE0/bbsA4eABMAC3NRnAb3ek+e2O91f/3uzOL82p3N+8GdWSRD95OYaUFSS1+XiWOfv3KxBLJrLYo6Bme4cmkF+SVDWl0waV516PDPXbHY+8/HLkS+Bp4AS/3XGu3+6Y7bc71n2Sr1x56+fmaKawEpBRTXtJVsdHQvJXWp5rEHbnE1SV0ZsXpbVEkWSA4MoUFm9sP7WK5F/GR4/hD6XvSKDY5vrmQ1UxDAfoGI6cM7a4tFBSVUoMhrNSFWXtl2mps1W4d6zxq9Oy0mdiTF221TPMT6ob6NoRDelBIKYNic2UF2nbTwCKZfkyS0vvnZacX5nUZg1lBgNJ4SSiVbbOiEYaBGY14nrZpxHqewCWm8zMVrt9BfDS448uVJEfBngresopJWoawZXCn0Uyqs+bUiM/A1SsSxoJuP12x2iEM9EWYOGUIxxX93mv9yvAKkSZ/AO0V1WWSIaaLHP27/SUhPVnrtJ1EXDfnCQLiiTRvaaWzLDpfURHBsJ+F3OUi38XVdr2WKfHWWc+8Nsd51G/DLEAWOAo8K8uNyUvBhi4VOXEBRXe1NpoPkDzXuVqy/5BFIPKwGUqx88Vt5AkDJKh5kpEWTQObmuzdtKWaaMMf0n3GD+uRbwv0NS/OVUlZ/rtjgHagAcAm8vbDrgX4e28uNP9H7ya1OaTZ5Pbjufl1hWxF/Q9QtsUAQ6nXiB/oV2HKUusMlMV43tA/6gk9bzjplU/4g5OkB8K/l/yQ5vH4Q5GrvpB6SOrJG/OgEUdJYZH16lJ1PRGzOLiy2PaP92kswBUieaxg+mbbFcB0+LOy3N6nFlOj/N6p8f5xZjXu4/UnveXbbVNfruj2V681b2i+zq1Z0otlCfBknbwcq6BsAH6r1Dpu1RigbAukSSFbwPqVLqnGaY3v+Keh7c3pbmtSQj1MggNbB3zlc7bruu+ecw9TyxC9Lc/IAayi4BcIxF+Md/WeYXlwuc6S+vDctKamKZhAlCOmJht60cU44SYQAZoWVNy3VXpT3NUh7u5OfX5v5YnXRy5xPjT9aWyzE8pycrZbVtNvz0nm7Patmo9M9Xwujlz1j0DqkNjd5L2TmkQoez0ONstNxnPuKFlDsM7tjt7dlfpElSJDVnw3UDRHkZPVUitVum8QaW2aNgfr23c1PzOopI/O9eK99s/FOK5ws2MK1rHu0WLf9vmFidWLnP1VmqFmqhXcaszgyuSb1UiMstawzsnyPjbQaWFItmkLI6/8PwpBA3/XiKptVnVwFuhDWdTFbg29vNQp8dZ5/233GQcclz7NhzVoa1zYlpqW4BNBsMJr2dmfPFRbs8WiEK8uHJjUvNVCzOZmJ7Gl+mptM6YqqR0fAOTdQGWFt9jtM66A9GY326I97sLLgCuv/xHhSfei9KmGBdwX04Zv04YF3l7wrhIJqIC9gfaGFRlaPfCagPAt4dLKBKoinTWcm8La3WRKYBQ+/VRIvxv9a/ZiyvWJQMqptQIqBJnzZ7iQFT89xEzqHQ5ec2FkhxtpirGGl+S5burg2X9nt+0ZVa7cJhKWc54MCe784PNmyFLCp2afasCHF8R4pHNRQBMTEubp72vf7VnOge31YpQ/aQAoUGhlxWby3v/8JrnnKpKaIoxuU9aTW28lzzylmGAoRwxk46Nohscp8eZiaqeAfDq+i1BgCKjjLnz8yvjTnsWmF2DmStrbqNkk3CFUGrl92vLTQ8AlK1Opmx10tWgfkr94MPRIsjrd49XrmtdpILQ2pzvy/OtDZcc+QGAKWOBeovxMz9AX2nZe7iDjz3VLOslgE41EqNrHwi+Ehk1vlK1KABVqvn6mA0+jvMQPhageRP77Y6BiHIF+AwYefIZT59uc3l/Gd/92J4AJ85RuPTnaN161Gb2CqlZ98qZ6wbVhADO/EthwPrIWMRM+x5fnq/RAv+EVcMdAKUrk8n+psg8+evbVvrtjuOALyyRGuWpqS8fiZghxTv7DI3bzzGmL7xWNlZJAL4kC69lZqzDHVwXyM9dQ72XNQh/jHMD+bmvI4WXGFICAEhy5Alfnm+uL89XsJNsHglQ0F4CSaJXuEKaZL4Pm7Th6Wmte8d8PpBNymfzLl84B/gwPujSFd8bHp4wLvL96y9E3nninUjo1q+i6af/pRRnVKovA2ctspi/m5CeFhvVPuK3Oyx+u2Ow3+6Yjlj/vF8Ec8fNhn4Aa1qCKkmsaSExq0tqJUDHTSqvW5vxbuSERSBGIvPN5pmLDOlqW6mIswx/7kib2Ashn4qAdbGDCxUbp9c++vkFtfd+MjD0at3JR8qL45djLW7Dlpp7jR8yxfK/aBd5wzWypA691PxlqTmrLp7QD9QvxT1qR89kSo1cD5DZpRJDUhQpguSLbmJOqsqCnJVDwtBrndHACe3bRP/XMkf+12we8kNaKmtNJjKjUfqGahhSHUrbUdr/haSqezaReP66Yc9mlFWMqUxNmXbjG9PPcnqcaQhhVcfYr6NLhy9Wu/3UT+LzYTLPvhElJc7d5ktHnx/u/Wr8SIDqhzJPKZHlT1pFoxmKKmOUFIC7nZ06dAWuQNi0BvryfGrP107abEhe3/z2BWrRIG80W0XlgYuNLGknoapcX/HvQz4Uy7//LL3msWhYurJ0RQpqRIw7SrPTSjOLKpqffMbTt4Oan9o1X5VNQQk4zZfn+xbA9XLn1d601PYARlVVflqzTn4ouxm/p6Zw3Fzll2u+V+pGTWEDuC80sLGVSrlhu7HNO7483xV79GJ3k6dvPOb0juuK37VuCadNGiTNmdJHPsIUUXn32WjYHGFbd/f1CFUOwDtRSXrjjd6jvnIUr2odlo2z3hgd6GffVGJ0f1WLocKAKTUyq+vspYcD+O2Ox4G7QVVa9i+Tk7NrCfyUgwpRCRyOAv9SzZHm8KQ2n7xuss7vEw72JbT+grxAfu77uK1SqSzf+EFG+jNvZ2aYopLEcZVV/J6STFSS8K5ZTwiZs9vXrZp5BFXt+8ymLX1PrKruCNyDENKfF6npKwbUvN4WYePkxeBzS9pNX9OjVpb49kIDNclVRCRpo3f9g0fURLIrA/m5RY3x7mM8caWzujBVSVrfLcInWzYytEO7WB3o5cvz1Q0KNUeTUYPXL+z6wMz33LWyUTUrEQsQQVJ/R5Xi12DOBc4tTeXGjEpukYFCK7VjR538ann5ce2BVhAdktrtcWRjJY9tLmJURWUpYMcdLHR6nBOB02s35lJTMjwKGFKpZo7lWixSmLvDVzzwbfefDkMsEUF7l+ciHKNm+PJ8R/rtjh8R6r+vgbNPPuPpG9C8yNPVdbw3+dmt2nH7o4pIa13zC3DCiNZtX3K/p17fugRMWeEqa5tQq5wv1u5zdKMd8fplAxeaayM5mYbqZkpmjbHXVDNqdcynS10LUoc3ep+27OqF33QGKEzOKr30pHu/QMiD/kB/icgbt2c+Oui11qF+AC3CUniTSTVZFDVUI0spMXWm5tBzLPBUID+3wm93JE92Zj3w9YgyV1G6vEWS1BY7Un0C+O2OGxFLP3n7BJkfBsp8sWZDsHskbPVGBzF57eFFN/z1VTZA55M3hS3WyOHOTh1KpSi+8U9G/tN2vCELPj4ZKpDZnAneknWUzU+LFC1OjzdJqsCpjgL/d3v1onfCS1cP/ja1svpwVZLDkmK+Ibt3yferfIbQ8XNVvjlC4oNjRVk8+0bkybZF3DndIfHcGQaUcMa0Hzb6X30xK/Ph79JSOwO8t76QATV1qxZ/A07AHYzitl4GvKOo0m8LVdvMPvLKuwAurb2T35V+1yG0mdPON/xKvuktlipt159Q+9QFQFUgaUyNojJVlrDG5/uxrJYVn2Za0pTarArZXJKDGKy9gFid0tOX56urr367I1U2KZuVsJzc5oiSXzYuz7QpW6QuHx0t8/VgGVNY5cgClQWdJIJp27sd3VhSevQ1pWVnAA/iDu7RwHSPZ8qpVdWnHzm7psWIaSVnzrqj+yPUr4esI6tCLE9Y2laiJF3ip/5bZ/os/4KT/HbHfX6749zAJ61t1ZOyX8/fcv4Pr68/BSUsAYybvGbdD2ZVvQ+4sK7SyxEfQOdFNdkA/3aUlixpJwG8XVGQ3wHFMhVYdnH3e2tb9S+jzaDSuntmFlVkGizRy4CXQJoVKe8lASg1zZ+3ubwtR7/Rwzg1Oalu5BqRJHlyaiq/p6aAqjK4QK0TyHM7S5iicMtXKlWaH1izaJQXCjfHnMMuf/b5jssKH2nW4AEUTNHwcb39tVntN6umk2cpRwAMWqKu0gTyWsRaufOANdQL5LXAdaee/lT5pC7DWz9x+EWhDkcWnT8kvLl2STuJeafUqgDhSuMAv90xym93tEK4+1OZxlVp3Wo2RZoZWdjCVisJ1fZ0v93xzeSJt3dPd7gWmqzzmwFEynsB3GNzeSXcQTXzgZIXLy6JZMubj56uqjK/pKYQlSTahyOh6aFhnBd8tsYYVmMV9n4kadR9OdmZmr5zJJqjx6/RwzoghEi1KRomdVZxj6QaiYxquHBlEU9uLor836YtI5Y8esnqyRNvT/LbHWf77Y49ia+729hcXjl3WiTpqh8U7nxXYtrfdvovqlO2bOXUMXni7SdOnnj7Qw/MfM8NsDSznXTyGU8bHAV+FVW6UTYq2qOqKpDnKPAvv3qswXn/xQaqzURbBjGfP73yZuAsYAgYUIoHRwDym2Vxe072YmenDjFHvb4A0UjmdLT1opUkM0OxbwQYJvvuUxXTxljeNK/0mdq//Wb1dZyEEMgR4JaTz3i6I2LtNcBLoWaBd74YVt9dpLYOlae1rgF4G3dQLUkyrHn6bANVZgiXmFJCJaZG86WwrazsdeTsmhb2mbKx54/JcQIZQGoHDDhz+R8x8xMtq0sys0JlVyDWAveXTFvo1PmB033FVf1O/0tBjiqqZ+PaEqOqUiNLSQhnLgAC+blTAvm5D2oC2QEsPdlX4nrs/SgtgsqM/xDII9AEMsC/op8iBeVEgFzDTB61vFvnZGnOiJiAa315vlWhVVfeFjs+vXVv5uR0W4zQajwyz65ODqZC6xK47WN48GOFBz5SKKi1sI1A/gZo29ACGSC7qOLYI2fXtBg8q7rtkXOCEzt/IpfGzBa1GUostsSKtkUiklybDVZFVaWIbCobenL7th/GBDLA1KStJpLHUK9K7gvwUfS4w9+Pnljn0DtF6QNCXf8XMO8fRWi4u0rrmgWSxswJJI2ZF6mRP5PEcqu5CFPYFSrwR6ohDaBm08i0cn/+ezWbToxV6HbAcqfH2RHAb3eYkNQvlLCcbEiKktq65pm/DD1nAHTdoGKIqtz1ucLYbxUe+SCKOayCMBwnAY8Dp11zy5opuIO37qlAhr0QyqEky3wAUxTWhpTbqHc5p0M4vBZVXdxhsyoBrMqR6FJb+0xNj9CMsFHdsk1SjyDUqi/XlhvvOPPnP046duocCudnqADtItEJswNrmvvyfAV+uyPbb3ecf7t3o7HrOpXMNaIRfjDCsAVAqWm+ArE8CiC9QO1w/XE1T2FuH6X9iCJScsRITAnL4yZPvD3l/6a8OOezN6eR+7eKbNnS2dz8p0l+izlcZjBIABZFeQjgyWzhhDy4QKVPQKXWAHdcbuD/zpQJJRHOKVfot6hlBGBsSal6TFV1/0u0MngnM6PL2JY5t+zp+90VEdmwcHUr0QBshZBelnzBDT9FYvbWzxwF/oijwD8BsW75J+2421Hgr0Vb7mUgOuMMw7T3+tdWpgC81PwwaXV6i+8QnuZfAxsA0+ocyi4ba3r2pJRzPjix5knecIyaj7B7Nkc4lr2OEN7tVJUNkYoeIYQdKBaNjX41bw4s33TCkMj6OtuuutpoPuvRqouqnvr1dcsbLypSRqUKYvkN1bJsXS7Wtx8Ry+9itaMRmAKkjSyctKl1ecwcCs02GADuxR1c6rc7DNozfw5s8NsdjRGi1ezvLtdWpEhqcqVEdqCM676rIKtcBejitzvMmg3/bS0fdc6EBc06graO31Hg97U/quiRVgNLaX9Usc9R4Pd99kzbkUjSccvbQkmb6DiAYesXgNAYnA2c/HXFNy/3CdVQbpD5IS11CPC+0+O0onmUGjN8o6lfOvbyAHlpLsCJzDKP/G3wCV1XJX9CvS/FMlS1FLAokmbHh9cdBf4AcBNiIPQLcFNSq29mfTNIYmovaQWSWpZtr0hHxGyOdfqvrsmRHjN2q14KECo2XRJvy21INraxFC/sYQhvbibV+WxM6S2xrGvdv//kVAe7qaAEzcJsMGrFH5+bcyZPSO36+IaMzk9x/g/hljdPUrjwd4X8aVWF7SLRFp1rw7E6WucR7Lc7ZL/dcZLf7ngEsQKgLUB2OVz7nfJfS66uj9v/X6AF1wPntbuveCaajdTasor0XtXVLfoFX9GCH56H22qurena5an+F/BynzOLHzniUu4dek3Pk894+mXH+ev/uqDfhiGHHVtIcouaus6+RRBmLWlWNzj4arD0B3COo8C/tTdgA1FutSz/o7eEr6PItCFMXayBSMgwBngI0f/MALCVlkrd1yunUe9FXcd4S7fQq5HTtGtlKjZYbvXb7bnF/6aet/r3Ziyu7JD2TXSw8k5k5NpTah5foyI/GsjPXZrucLU1pC3OXaG2vrNStayXJJKAS1ZPafbJ0q9a9Vg3PatGCUsn+j9t85j/0zZLlpYkr1pvMiKrkhqpcACcV1s8In7pZg4wRQuL+iCqdJJkUGg3tKTQmKT8NL2NWEnSeYPKuPeiv/YJiNfdqhSefiu6Mf+dyI++PF+NL893b0zzurfssVC+8fXpZ063m0sBVgVNyQCHhUJlvpWr8a7d8L/2m/k2o1q4/69rzrrlZvN9D9694shRpz4z9OTTnyL39CerI5J8FWLk9yeis62jdFmqtP7vzC01ZQZUlSv/7dP9RmAZ8MngJcrwx9+PYoxKVCQbov+2FZ1L9frzHtOe5TfgdGD1crUtw2ue54Hml9F2RHGJOT2CqkjNAb+jZNXVJiVK3m9ROm1QseT8ElubTG5FZUmNLL9HnINK7kzR2L8+UmJVS4kas8S/rdsWA4ycYVkG9B9VXtkDuP624tIHziqvmJCqKIWpihJbF9xg3PbylDf+HTroCSwKMvDloo0vWFQ1tgB/Ysx26Cjwb0I4VGQ7Cvwx2/ZAgFuMX1QBw7rVhqsByiwVjD3qFgPCBl7XuCcMlzOAjE3Zi/+3QU5laVaHeYgO61HEiYNNEVUsNZC4CdXyo3ZpTEXKVb5Jz3z57T084p23AuHle/TCS+dPPnfpbyVtK7eQVqOmn/j94OXl/vwT0BxaFlvMpQiBMBLAr3YEGJfucKkjV/2dAbDOKvqBTSutq6iPmHUCYpkaCAepPf2azy4J5OeGzv16kSWtSk0FRgPIKhxZIEzlYZl3EYPNWICWOiPW1DZ9IC5YQkpO+O2srlWkta7pg9t6Vo3EFwD9amoYclhxl6gk0aK6lKPWzr0/kJ/7ZSBpjK+7tPnmdzYWckR16Msea1XGfh0dcMx85XwtyTUFYz9aF8jPvQhI++Lbe99aPb7ZQ4GZzTet/iGbK2b+2vWeLyKjX3wufTVuq9W3cvXEIdWhzOygSlpIjYUKfFYzS4zW/n9Oc5briiTx4ijD1/bRG2altqwF+Bh3MAjgy/MFfXm++xy2so8kg0IkZGjDTmx1+0rex/Oyz/16oXnE9MUGYPB0u/TRx8dK0U8Gbr2YRILb0murngHorE450dJ8ymjZVNb6rGkqwxfVT3B7raAVQJtIZJN2KH597LsIx6/7AMIGKsYPF92mY426w7X+frvDCpyh/TvAUeB/dsFlC1/15fkmaMfGKiqqgsSTPcbMzLZX3oQYCGchfEQG/tphAN92HupCC5gzTPY9pKpMBKy1KeYl1wy9o+Lk059ifZrwNRy6UKjsXjhN5pOjDcPPuGaEw+byNoo9+ep3ZjlfOs3geGSMgStvlaluJbrKxe3loPvNRdN8eT63L89Xog0KlgLSY+9HDYgAL/0R/dIggIrU9UkvttvC2I1XsfTrlqyZkj0KpG8L51hbVW5Motv8NdRgHnL5o+Pbr7G/1j/d4fI4Pc5rgDUp7d//Ic1xz0upUs0TAJWF5pcrNySdA1C+Jtmy5IvWvyM+evRn5IfMjn1XKHSprd2Mahbri1VjasVSV91ztd2idnz1ecMWhCMgbY4oJSWn9ipb6GNlWht7r4gMOWVg28SxiH7yOSDSqpRWnQsZ7bc7uvjtjl2Hct4Fe+XotTFTmQnQQQuvcVZ5ZSygx6yRs5UtABuzoNYkeXx5vpD2W18kCUWSFzr9i95yFPhPdxT4hzsK/EcjFlr3RRMIwUBK85U/5Cirfs1Oi9YaXgQyQa1f0Q/M7GKUkaQcgFg0IeD/Avm5kxCFvmIzmXypjHjbaFJfazu0GINZqUAshajjwU+iWCvqG+htxSW/+PJ8AeAa4LeL59f80329MKT+2F+8rrfWFypftD2pJUDvLYHWE8ZFFprdwaW4g6/K7uAjD9248rxKWW49KznpSRqBkYZZ9xtaiDjGoUXGnGjIALJK9zM3DAdqcVvPA3AU+FVHgb8Y6gLCHyOjMMbwix2gTSTyvKqiGpI2EkkpPenkM57+AmhZlCYNu+5aS83MHuJ5ZVOpZLLOAliiNbQHgCIJjB02kYZYPvYFIpQpwDXd7vh65MvHj1l61vI/DjOqCr2LVnaZMC7SY8K4SDuA49bMrlMvt6kIxjwgFwBMSUleEv+8fqXDZuAXx2r1BNsmNQng5T7nTosiKYZqpaP/0zaxaGYXa9s3EQ42/x1bdx9wFPirHQX+z4BbAI5aqNB5g3qqSSEWxvV34FJHgf9IYNBXXYa/sETMlO+si1XtDq6jPvraF7OTklIAhleFSLaEL0jNFBoe1+IPM3Bbz0KYIbCocPsLhrce+SDK8MUqIxaqsVnu/Fj+Jk+8vSolUvMecGr1CnOL2jLhbpBWU22w2TbNqNpsWlbkTxt1bEk13darSEgUJWWsPvmMp+2IMIitgVKE9zZoEbM6hMNFklTXKT2w7Xsxp0enWDtVa/+pjR7/2lHg//uKiYsvuqOy+P+kVmHmddJmb5nhVRffZvj4iXMlO8DAZWrG8IVK+d3jo3+O/lMMstPaVs8CqNpsRo2CLAb1AEc4Pc6cO8b2Go/wko5GZT557zh5803XGNLmdBH3MCr02Yk2YDBiULmCei/4etzBP5+PnH3jqNrHmKQM7Yg7GEX7mIGq8uZow2+xAdIsxIBTucDwy5GShNmndFrRv+b1bkvUDm2QpJUbUloWxye9ob0CIKnR1E+ABTaXN/4rSw3JMqCmLEnmmguzv77odgMPXShtZ8oE/tC2w315vqjmFPej9mx+AEPyOvqs9IG6/atsXV5UC8x0epzdtHsupT4IUm9E4Jg3a4LGuRtnx0zIKipqBOEs1hxAQuLC3xTOLi+PBPJzn06vrbwpvbaS9sUhQhvFTP2iXxWyq2qsAKWdwvzYLYueoXf+Bu6uTi/v8+PWZtj3HQX+W4GHtf8/0vI3x2937NNkYK+EcqCV8gNA541giKoMra42AMVAYNhi1QiwOkcCmGhzeQ1aYI3HtcvnbJueo8Bf6SjwL0DYfL4BUKOyXL3ZApJKZpfKv+yjN6S1G1GEikpxGnw1LCKJ85JAsajAyYH83G8BAvm5BYH83C6INYRXA7OTMiN0Oa1wM6ixDv8boDClBpwrxAj7tY2beC005jiby3ueL8/3zoRxkdGnTZY7AhjbhxhOVeDrtesxV7dbPSvbTqUxqcaAamWbdZ4AE8ZFhk0YF2mUJVHdHl4cLqzOygWo3qwtNcgMY7Coj2rP/Clu67ZrsB80EO35tOk1NVsq7wSEHLXh/5Mk4YFrafkNSLW3nnzG01WXn3fyI0VZUYsSziRUKNbYGdMKQOv0tU/JzQbovFEF+FyzrX0KLDcqkTa3zJ0w+di1c7cdNb4CfOS3O67NDpXVLfvoVrIGxCzeB/BbSnLpOjW7GOCPqJMgaf8XyM+NtC1SH5RVKEoj+NWb9wwzoMbUtB/57Y6jqF9/+76jwD/DUeDf9utIjcFHKtR03gj57wkz5tSeEqPvNj7vKPB7ABwF/n/ecJ7+P4RmKAUtoI3G3WjL7xabzRGApJrMlQDJOcKrKrN11UuIQU+Ml1RFqvv0X6/VamtrhYopoi7w2x3pfrujDbCJ+hnfBoMl+o5ql6sBqv2mvqt+yWm+aX4G0vRuhW01t7glLdp1QHikxvI3LpCfG3Pt6g1wQVlFLKrYVNzBmP0wnunNulXGBMWodSd13C9BdU6rrPrwrcJN6sejVN48Seaqiywda8zS43O7yrn/dJOQVRj7jZJ+2Ap1GECzHhW0HVJyG6ib1IhM0ZK0J6akJMfWXZ9iiKoTT5irjAb4ZpAkXXCXsfV3g+ScIqvE2hzWq0KTFgsmsi0xoTptZ59dfD569seLVBuIIDg5iDXff0gSpnuNH6WAWgssCuTnLpdRbh0oi4UJD4cv7hzGKCMc9PrKqlr3ZaLCHJWRSgUAsmWjAzEBKdnrl/ofaBEC/QAhc2X/WpOEKknrd3BqLFDKVloTrb+4HSCnVGXwalGVMo+u+oq4GAbpyhY53eH6VEtnK8ctgLQq9Qz/p21uXjE5p0dtmQnJoNbMOlwZPvZa46ZnT5e5/yIDV401UG0G2yYY+Y/aOjC0S9fx3z348ITvHuT1X59msC+V6KI76LtS3PeTky1cfV4SD7UxoXZ4f1NKx1cfTe30IuNHyMzuIm1EfDDpf1oWXkZoOeqyzA7kwZ6wV0J5djfpq4okMEfAsVEhR4SW+8cW+thaJWcMBkgK83W5P389IjziLMRIWyHO+WFbHAX+dxwF/lGACdQTrLaqpR2P20Lrw4ODJZkW6W1qVrQbUnL/zWO6bS7MEqMWJZwJcFsgP/f7bdML5OdGtZBu3wNlBpPaqcupm6ZmdKxyIb6aNAHAudDBtFVrGFxVw2fREVloYT+B+1GlFqbUSHW3gSWM21xk6xyO8GTk/OaKJFOUlBGb5TzptztSoc4G9RBihNhoM7Xhf/79c7XBXGczTW6+XTTJ22M7Npd3FKgPPml6g7MMf0oIIXAJ7uBm4BFVpdaYthRz9h8nGNIWl5qz/zgGQAm1/MGYuvQEAEPyWpBD8QOqeKH8HQjV7uD1vtFPTn2l6Ni1c4hKkrqwme0e6mNWx3gVYEuStVJBomV1KYdtWjLksh+j0eSQSlQ1DT265plml9beye3ha5cCL951Y69Bp/yjiFG/xGQtHRdi9tgNMTNtTv1XfPYLjgL/Fql+wAnAz4fJEBcpyOlxmtIdLjPa5zSRqwd2y39AqD7dQR9wfKksf7TeJOLSPlF+q/nh8MUsTu74B0B1kdmCeK6PANeqX7LfAPoqEF2nKSjP+Fvh9ReiFyM64RVQt9b1KUeBv033+f9ecX7X+2//19ZuqwmJUprasp1YekUks64OpSGiID2r5b890BlVVc4or4hpIz5kR7iDtRZr5N7mvcppN6K4Kr1taMEOz2to3MEFyarqOLa26vaf+ssUp8sgVm/wwbHyA4ilXmsN5uictsOKadGvrFg2MB0kL8DmBRmdo5K0ANFfycMXqUPab4GyZPhiqCwT5zsTNkrnS9oAkm2EjebXEAv/GB9BcCsC+bmliHcMMAh3MIIwv2CVqvjANC4USBojA6xIumhSS6mUsGpgkWr7BTglkJ97cSA/t7xFVUmdUJ7VYwCDqoV2xZCyAlAeCuTn+vfsRe4RsfXTsaBROxLKMe3DIL/d0Tz+B1+e77ueq5TjX341ikGFOTldGZz5ShpGzoqdk10ZMaZWq6MRH6bYgOizP0L039z8tXIJMA6kFOCPalnq/eTxlvs2ZUlt/uoplyxpLz0ZTJNemt1V+hKgdHmqpKqMlcTSTwBunvcZI1YvrTUqsD4LvuobAc3Ib0xdgSFFLH+vtkg8MdrwgaPAf3ZMA6ltByPs6LH3sbPohLvFXgnlOVcsXBloJUUARgVEJahRTd8D04st6acBOFdKHyA6YztiVLkEuCOQn7toh4nGIZyVCn5uc2Rpn5Tm4Zhd5ULAnvFO4aMlFcfeFg2JpTTRqs6V1Kszdow7WIkmaM1p0SvbDi7Nd5y/fjDCqYkRa+YrKVUSBWoHysRX0+wXXPLEMIQKm5QWtXcZzGoYYInS7p+/lF5pwJqsmvKxiBmJE/jcb3fcgmiIMbVeo4Z5TI7WXlRpTJoeNKdGUrtrzmyqFGvovaDu+7JvjJKnc7ZhKoiyOAd38DMAX55vtiQJYWHJ+ZmU9u8bJUMINZq82Ji+5Dxj2r9/qCq1kiFEeg93nY1qVY5YU9x9nQowW5uh3fzATM+XjpLV2UCVQVVPOXf65HGIjySAECx12oPmoeAHRUkZ8wEen/7mqSfPVl+/7jslIsmRtGhSIfZ5K7/0THxsw+SJt79y9p/Kj+2KoDyJmuxyYfNxFPjXs/W6U4AVjgJ/FfuXRxAj55oKC/8WiC7qCACnxykj4vyuSetxb4lk3lyc1u0xi6XlpNVOj/Oo3u/1fdbZqcOnwzu2uxBAVQxF5dGWbd+JnlyyYX32xQA1pSY2L0zrjzt4Ee7gE1WbLUMBIgZmTjpSNOHcf1TSamiP8Ly2AMsRdb7OC7rMmP7Ozf1uWXz6qfncdNTNAPQoWUMPbRXowk5bTXyfCeTnhgG61tbeAXBYTY2cpqpmhAPbztfgu4Ov5TjLD09vU3NFxjuF2wZ6aDzcwSW33rzqmb6hmrryl1VVSU8LP+Mo8Oc5Cvztu59V+HtGuxCSxHeaIHwBUSfPnTAu0h9RjrVH+dQagNI0XqpKkuKXdl3qy/NNpX6N97ZhGp9BhPeMUh/feWfENDnHaPmvDSgtlwMMNyzMoH6d9CUAJik6Y3H+2ccH8nNjg1KOn/3nhogkn/6K84x/ZuYchrOmBouiIhsrSe36ZGNH+Ppim/+3E8qOAv9qxCBZZpuPhPjtDunBj5W6icvP3WxR4ISTc5/5aNRp41iTKcatTuFUdTHQxZfnO8+X57sImNBmi7qib0CN9bE3AMdccrtxAMJ8GQKG+vJ8d/nyfGOHLVYvkwxKpLbcSKjYfJN2zZvAipRIDTfP+9wMMKu7WE9+75ZiJWX9iTdGyh1fqlFL/AD07x084ypHgd9NfZCZE/fFyXGvg4e0qjZ8BnDkaqGyO7bmaZesKo4O5YUAPHDk5Ych3MTDwBGB/Fx7ID/3/3aS3I5xB0O4g0/gDl6LO/gx7mAYYPkDt3xQtXLs/RVL715UUzjqrEB+bvWukkKo5C5GLEYHMYr+FZhhUFR5/YxMVoear0Qb3WbUVj4BWGpk06pR3R9/E+i/Rsl56Pza+2Lu/M8O8s1Zj/CKjSCckp5FdMY1wBWOAv92NreGxFHgVwYunDt0QfMulgtN9xU+EM7jheiZsRX1sZjGbisVLR81vRMThg/jDk7cJqm30GY+qoqiqtILkqF6gOa8UytJdRF16r7b+9ZIQ60CtN8CE8ZFjkTYep5DhERdBRzlKPB/r+VzGsJb+0hEB7QJEXHruecPO/fGJZmxgTYMXKoaZEUlPeP3olErpp2GiFF+SXoI68ZMeONk+Q5Hgb/Ov0Br9HVaAeojf+03NNv9s0DWY+cbjlPFKLuT9tGPkYhnyJbk6NXmZtNWSnIESY42VyJpv0iScgtxn6dTanNi6sbJF079ejXasqUtCzOu1AJDGNBmCeYo3n+6S58UaStaVeHUMhohFLo5CvwPOQr8dQ6LgfzcEHB02GB8ZFlm20dqZeOSpGiY5kExU16WE0EyBosR6XwCcNsrXUasNprGApxVXglCkFyCO/jfphl3cBbu4Kd78z73lfuKiuuChZhVVf5y3UahTnRb7WgDbWAigKPAPw8xyAB4xZfn++nSn6Jdeq5WDQAdNvOiL8+X4cvzSYDsy/N5tHNjTlsj/XbHYVAXDe1m7fhFjgL/1qHrtifmpXuhzeW1AdwSvmFh3O+v4LYOQnM8Yusvj9Xh9C+a9E2XYceXkva2CTiyuloFkE2lZ+zi/vvK72wdbWxHM2Wo/xLgTVr9jXGupGkafuonMaNPSexDOslhg4k5mvFrzO/KL74834cTxkXMfrvjCb/dMWXCuEjPkbOVFQArW7LFUeB/ZfTdRgktxjXwjC/PV6clcBT4yzI6hN6O+bHKRuVrRLjSrT4otKKjWnhHUQnnl1fMnHH3My/7b5xw9sLLZ12M6PvuQJvI7YQpQDVCc3Ca3+541G93mP/j/B2y10K5eWHkfYDoFhPPRc5iHTktm1eVYlEihGUD81p0j1WkhwL5uQ2uTgzkj3p05aNjegfyc3/c9dmAO6jiDn5I/ch2sKPAr8om5SFQqdyQhO27dck9i1Y+DZBTXToE4I+2fTtuSc58Endw4fDa51NLyMhGCKBXABwF/j8RtvAqhKH/cURwjXca8nn/i6t/Ha8sULtMej96Eu9HTowt58i56Z57+wHXjDV+RYZULSNUbvnbXu/L86m+PN/FQFdJImfhpQtujnPQg/r4wXVCeUk7ybGoY91g8CfqBcu9QF9HgX+rmMOOAr/XUeCf6Sjw/4FYVpLjKPAvmd3SPvd/I27kxqNvRYUao4J08iwV108LmklizTVVZn7zHi5xT54hMsMuv8/2xC99WriD3/cLjgJ/9fI20jrEwE9G2JbeiztlgCljfp2OWDZW1HVQ0VCrMlUxfhZaf14skkKsXsc6tBsQs7O3EU5xIWDCX9cuHPPTYfKgLencL8FwR4H/M0eBf97ObJmB/NzNgfzcB1Y+cdoDZiVyc/xvG5pJWLs8OXe4vKB/IGnMuODDWe6lJtP3tbLE0KpqRlVUTgJOxx3cnUHwAcN+z5aPY/vOmlqASbitaQjhm4pQqU6Mu+R2xORhkN/uyDtlljpcElouv6PAH4syF7ODAuAo8C9G1HsjMM1vd7xBvUboGUeBf3cGJN+ifSAGEaf98Hlq174jap5FVetWQcxAxOP3Inw2dkggP7fsR8tdVwOhMyoqJQCjqt55+8udR+5GPvYK7X2chVg29y9xjobb8ClCG2FGPGuMWwCm9pL+fvNkA4as+RvRVlxIhnJm20VTaVlKf7/dYUSYBO9EDHKnjJyjHg8wzSFnOz3OXtr9D0e0jW1NZrQ5onRsp5GbV3U8bgvdz9r4u+P89bX20es7mtMisYhhha+xfsElZeWwjRbAl+eb7cvzPe3L80W3TTeGo8BfTf0X+r5G9IV7HFVwX8JszgAIVxh5t3IkwCv9Ni/9AGBTclYsXPIDxD69mDj8g5jZdsBt7dHj7I0p7Y8qRjYpqhqRWz0z9eWBwD3dS9cAsDSrHdTbiI7RtvcH8nPrwtBoDj0ZjgJ/N0eB/15HgT8+3OL+YjxAMRkXVamWEoAI8l0dpELTpYYfYg389pi2YUf48nzLfXm+4h38tJ1QBo6eMEJGqY8RXgXYHQX+xx0F/uB/ZVRbSx0GCOTnViqyYd3yzLasSm+5AiDvF4V+K+ssn49eeptxgud4AxUp0p++PN92aWtq7BuAj9mVKaOR0TqqmC3xG7ZeljVIMlQ7AWqLhtXN9mu2HEPVylsyKpY8mqLUtO6FqJ8xH4kJ1Ht4ngjkIYb7lzoK/EsBHnpj0T/D//E/uhszs235CbH2FiUnXCvJEJWjx2W3e+s1BW58qlnmgyvNpmRrNKoOrQ4dIbuDp2t+CE2BodmR6F+PbKkL7uZHmHQ2AmM0j2cAHAX+NQiHHRA+L7H2HlNR74yLEb4VyYjoUCZEee3Wt5y1PiT24Q8ZIdRtq9WWxSWk3R136nLgWu2jIjtHaC9WHltVzYiqanKiUUPXcLjj7uRlb/Hl+Tb58nzHA3Zfnq9iR+c4CvxR6kNltgfQlg4NBhTv4XLsPXdBaC+Rk9fwb1uoSCIqCYe6LcStH0ccIyKj/NNdkhCD8Vio2od9eb4122XEHQwnZUY+TcmpRZLpChwhydzV6aTNbdPahF4xpUbOkWRiUfa8212/e9yDmLSB8Lzf7oMou2KvhbKjwF+Cpip88bdnrwrk595w67zPfgYIy4Y1QJ9Afu4jmqNV4uAOVlAfVOMO4My01jVk2yti7vz3TJ54+52HF4pQtkuyOoDwkGwLxDxPt4qvDXUV70DyK2J0Zp6ldM8CaC9tGn6n8VOMkiIBP+AO7p5WYXtiFWuA0+OUtXjhRyxpJ/FnL+kshB1ugKPAv2TnSfwncwDe7D2qR/zBr4+QPkYM7GKDgWnsBEeB/xVHgf9CrYM90EyO24+ZOCoQX4FKAyrM2X/2AM4HJkYq7DG7b+xzMl8E8nMLoU41/iBi0LEBoT4+01HgH88+on2NahhwT4ce5a9cpgW++SU1ZdQxHdrydXoakqoyMFRz9cX/WzvzPxNLMHx5vum/X7F4SNtINLYssR1i4Dgad3DjDi65A/Ht63TqP1v02w7Oq8NR4C9EqF/vQgyKbwDG7ElfEMjPnYXQtEH9BxheaPbQuicQy9KOA3rhDq7dzSR9MvD45iI+Xrfx72tLy97Y3bzsCzuLbBZHzGHBpm2v07Y/rWgtxZaNddH8GM42WWcvUWSJuZ2l2Ow75nn9EeJDMGcBY787XLp2Q7YUe9/lwBm+PN9/TQRjArM7cCuAbFJpP6L4+q6nbZqK8MdYh/gYyx6jLRnthYjuN9BR4J+6i0u2Y18dkb4CeuWEgtdqEYyEBCsv/CmQn+v770sPKE8hvkZ0HpqRIbNz1bObfRldEZ8KzNTO+/LfzPatgCEIFVfM0WnVfs3tbhDIz1VtLu85wBOzlB7/G2Hwca7hj7Zd5fWoKook1X2Qe2/wI2wlaYj30woxK1j70ijDN9d8scsGuSt+AE6b07KHfO/gqziudPzSz46t7FaUIVW6PD4VjzMmlGfv4332Fx8h6lYYuNKX51vl9DjPQjgrAvyqLSkZD4zXBnzxHtzbmT4cBf5X0EwmDYk2uB6H25o0tiR0RcBkSv8pNYVigwFUtUaVpGueu365Z5cJJS73I+puF+A53MEd1iFHgT/itzsmI4IPgegXtnPq2cF1ldSHI91bvkT4dcQQqlcxeNjRAOK/eBMYbVUUgPt2ObvefyxBOGXa/XZHJmKpKgg7eUxr1NnpcUqBfN+XTo8rH+DdE+VxwxdH+yJ8ZF4F/tD8JL4CESnoQ49zPsKk86Uvz7crLWVMLp3wH+d49uW9aVrAvdaW7qtQfgExShyAqPQxVUnCCa1t+APhbBSLOlJkTFamImwVAxBqlTWAB0nS4g4L+wewKZCf22hfvtkXAvm5EeC28+9ZNAA4qqss/C4kCa+29Gav8OX5Ik6PcwrC3jMdMZsA+G03Rsi7w0eIJQXZc1r2qC0YnPqswVL1CtBHc5aKqaX2WBV0INC+jHT0NocfQNjdi6h3RgEgkJ+7zubyLkI8Zxjt04P7FXcwJLmt3+Rv2jLGaU1nvsXy0y+pKZf58nzrdn1xAuMO1lK/xHFXvEG9UF64KzNMQxHIzy2xubznIfwH3gvk5+6pIK7HHfwZt/VKhO38gH1feQfEtItOhKo/TTv2PWKAryDW8LdyepzNEcscIxXJ0o+OAv/nO0ivDl+ebyb1cdx3xbZ278cRXtMjEeaIAnYQFGd/sk9C2VHg3+y3O6YhOqCTaCpCWXyF5FHEoALga9zBqMNNEcLBpl7N6/J+TX28Z6iPUJOw/K30umR6tOe8IYbFWYpKVJbEetN95B1ExY0JZIUG+jRlID+31ObyHofwiP/eYNkcUzH1A0Yh6uk6tIhWTRFfnm8F/z06Pwth13wzkJ97oEwhj5jhlMuC5SEoP2dvguk3ZRwF/u/8dscdiDr39K7Ob0gC+bkTqPfo3jfcwf3xydg9JRZu9jjqg6s87Sjwqz6odXqcaxDyowv1YXP/avBPf7qDNbitkxGa0rWAW/Oz+Yr6iIQHlD3+dOO2+O0OF8KZayYi6k8KcISjwJ/YNigRI/okhB3n4/9yYLG5vOcinDCCwMhAfu72ofMSDbfVgni+pbiD+xxAwOlxSghV4AmIGevzWjjSBke7V4CtoyU968vz/W/HV+g0GG5rM0DFHWyUSFA6hyba0qBN1NuGNwI2R4G/BsDpcf6MENjfIpZPKohP9jZ8X+u2dkfY/5/GHUwEH5StaAih3JV64zkI9cBhO1uS0VSxubxmQI0FVNBpXJwe511svXxrgC/Pt98ideno6DQsfrvjQepNCbc6CvzPxX5zepxPs7VZ5zFfnu++/Ze7xGGfI045CvzL/HbHr4gPgQM8f7AJZIC4GMA6+4dnEGquU4AndYGso9PkeQTx0ZkI9YFTYvxIvVBeCjzGIco+z5QB/HaHHeFFV4L4WHvNLi7R0dktnB6n7MvzJdayOh0dnQZFM1ldgViL/Iwvz5fYfkmNSIMIZR0dHR0dHZ19Z18ieuno6Ojo6Og0ILpQ1tHR0dHRSRB0oayjo6Ojo5Mg6EJZR0dHR0cnQdCFso6Ojo6OToKgC2UdHR0dHZ0EQRfKOjo6Ojo6CYIulHV0dHR0dBIEXSjr6Ojo6OgkCLpQ1tHR0dHRSRB0oayjo6Ojo5Mg6EJZR0dHR0cnQdCFso6Ojo6OToKgC2UdHR0dHZ0EQRfKOjo6Ojo6CYIulHV0dHR0dBIEXSjr6Ojo6OgkCLpQ1tHR0dHRSRB0oayjo6Ojo5Mg6EJZR0dHR0cnQdCFso6Ojo6OToKgC2UdHR0dHZ0EQRfKOjo6Ojo6CYIulHV0dHR0dBIEXSjr6Ojo6OgkCLpQ1tHR0dHRSRB0oayjo6Ojo5Mg6EJZR0dHR0cnQdCFso6Ojo6OToKgC2UdHR0dHZ0EwdgQidhc3r5AUiA/d8Z/nui2GoFTgL9wBzc3xL13ka/U4cYZF7ZrOd72W5rxyb8vW1S6r2k6Pc7+gOTL883e9xzqNAROj/NwIOzL88070HnZE5we5wAAvS41DH67IxU4AfjRUeCv2h/3dHqcTiDNl+f7K+5YEjAS+NWX5ytrxHvvl/vsirj3/pOjwF/ZmPeyubztgd7A94H8XHUf0xoKlATycxfvzfVOj7MfYPbl+WbuSz62ZZ9nyjaXVwLmAX/bXN5muzj9JuBrYH91Qp7Dcjyvf5thvtuoqv89YNgNtEYwG5jl9DhT9j17OvuK0+O0AjOBuU6P03Cg87O7OD3OZGAWel1qSN4HvgLe2o/3XABMd3qcLeOOPavl44tGvvf/aff5qpHvsyve1fLw7n6412rgO+DUfUnE5vJ2AP4EFu3N9VpfMxeY4fQ4M/clL9vSEOrr+I6w5U7PEpytbds3wH13h7O/SUsFIGgwdG+A9FLj9tMbID2dfad53H6DaH72E2lx+3pdahjO0rYXHIB7t4vbv1LbHt/I97xC2x7byPfZFedus90fHL2P13fZx+vj+5qcfUxrKxpCKEuNdG6DsE/6je3Z7/nX2SVNtUyaar51NJwe587KcH+VrV6H9p59fXeN9u4bQijHp9HAMjDhiC+Ig/1ZmyJNtZPS61LTRO8PDhwH+n03Wtk3tFDW0dnfxDcOvS7q7E/0+tZ02dcBfKOV/f4Wyvtffd2wd9RHxolHUxXKTXVWr1NPvD9NfH+gq68Tn319dzsr+31mf6uvm3olaur5P9jRy0dnf7Kzvk+vh43PgZ4UNZrZVldf7xlNdVZ2qNCUykTayb5O06Ep1TednaAt691TdPX13tLAwyl5J/s6B46mOlDS61LTZ2flpquvE599HRQ3GaF8oFUKjU38s+oNIvFoSmWi16Wmz6HU9yUaDfm+D2qhfLCjz24Sm6ZUJvGOIk0p3zr16OV2cLA35dhog+qGFsq7ypyuvtZpaHT1tc6B4kCXm65h2Xv2td9otPa7v4VyU+dQetamQlMtk6aab516dPX1gSOR1NcJPVM+0CPHxuZQetamQlMtk6aab516DvTASh/MNQwH9Ux5V+np6mudhqaplklTzbdOPQe6DA9lobyvXbuuvj5IOJSetanQVMukqeZbpx69DA8cuvp6N9M42Ef8h9KzNhWaapk01Xzr1KOX4cHBIT1Tburqa30ZS+LRVDvGpppvnXr0mXLTpSHV1/pM+QCiN8LEo6mWSVPNt049h1Lfl2gkkvo6oWfKB3vFPJSetanQVMukqeZbpx69DA8OdPX1/kRtPM2C3ggTg6ZaJk013zr16NqOpsu+tj9dfZ0g6I0w8WiqZdJU861Tz6HU9yUa+6q+3tf212Rmygd7xTyUnrWp0FSd7/S61PTRy7Dp0pAz5YQWygmnvm5g9EaYeDTVMmmq+dapR9d2NF32tf3FTwZ09fXu0+DhaPVGmHg01TJpqvnWqSeB+76DHl19vZtpJFTFlBtXKCfUsx7CNNUyaar51qlHL8P9iM3lbcjBq66+3s3fGxQZBbVh76g3wsSjqZZJU823Tj26tmP/0pDtRPe+PhA08kxZb4SJQVMtk6aab516ErbvO0iJf8e6+no309hVevu185F09fWhQFMtk6aab516mqrnf1MlUWfKCS2UE0993dBJ7nhf58DRVMukqeZbpx5d27F/Mez6lN1GV18fCHT19SFBUy2TpppvnXoStu87SNHV13uRRkJ1Lo0glHV1VeLRVDvGpppvnXoStu87SElU9XWTninvZ5uyrr4+BGiqZdJU861Tj16G+5dEFcoJPVNOqIqpq68PCZpqmTTVfOvUk7B930GKrr7eizQSqnORUfWZ8sFPUy2TpppvnXoStu87SEnUmbKuvt5d9IhehwRNtUyaar516tHLcP/SkO94X/2DmsxMOaEqpoTS0EnqI+PEo6mWSVPNt049Cdv3HaQ0ZJvR1dcHAn2mfEjQVMukqeZbp56E7fsOUhqyzejqa439rr7WbcoHPU21TJpqvnXq0ctw/5KoQjmhZ8oJVTFlXX19KNBUy6Sp5lunnoTt+w5SdPX1XqSRUJ2LJOnq60OAphrQRa9LTZ+E7fsOUhJ1pqyrr3cXXX19SNBUy6Sp5lunHr0M9y+JKpQTeqacUBVTV18fEjTVMmmq+dapJ2H7voOURFJfN5qG7qBWX+ve14cETbVMmmq+depJ2L7vICVRZ8q6+np3kVBRG/aWTdV+eTDTVIVbU823Tj16Ge5fElUoN2jZGxsgjZ2PGNxWI5CJO7ilAe6zx0hEqJUbRig7PU4T8MZWyScYTo9T8uX5Glw9kOA01dlKg+Tb5vK2AJoD/kB+7n+Wvc3lzQFuApIAdyA/t3Jv75to+O2O+L6sQduAzeXtC6wK5OeWbvNTU617TQaby5sFfAH0B36N++lAq68brewbWijX77ut7YDfgK64reOBy4GcuN8l3MHdajxOj7MT4AYqAbcvz7dpd66Lpq7endN2lwHb/J9QI2Onx3k+8JrT49wAXOvL803Z33mwubx9gCHAe4H83NB+um1G3H5ClckusMTt/2e+nR7nyUChL883J/64zeUdAXwPJAMTbC7vJYH83JodpWFzeSVgEnCkdihZK6++wLPAw4H83O2cMGwubwai7s9McCF+ftx+gzV8m8t7GfAOoi87dpufu8XtywBOj3OrsrS5vJcDTsT7LdmbPDg9TgnxfKovz/fp3qSRyGgDSzdCHn0DTI0bAD0NHKPtnxl32b629Tb7mNbA2I4SSUu1uby/AUXAubsaHO+KhhDKzeP24x/uUaCrtn8eYqRTd+6yb1qkhz91fAz0Bo53FPiX7Shxp8dpASYDPbRDbYAzdpUpm8trP6LFAmJTdIui1O76Uf6TPtv8nzACwOlxZgKvAFbt72enx3mML8/35/7Kg83lbQlMA9KAETaX98J9rZy7yVFx+wlTJrvBYXH7O8230+M8DvgOCDo9zl4IE0oQqID85xECGWA0UAJcu5OkhlMvkAFuiNt/EEgB7owdsLm8ucCbQGvt0LfAaf/9SAeU8+L2qxoiQZvLKwPPaP8eY3N5BwXyc2fGnXJy3H6sDE+PHVBVKhDaNQPQwebynrOXbeJmxMAJp8e52Zfn+8Xpcebs4pr9gt/uiDfpRff0+t7v9utgSM2bGK3sHmsPVwGFNpf3MITMuFw7/jaijNO0//e1rR8Tt783aR0X2wmXHn4mcLT277HAL3ufrYYRys64fTGNd1uTgHO1Y/cDD7H1qJKVcqvX27ElF2BOF+np0WI2cKMvz/fmNumfRL1ABjjF6XG2Q7zUgC/PNxXA5vIagduAYcAWoGOyZW3dRSqSpM0sgsDCQH7udhXI6XEmAYOBOb48X3Cbn3ts838iqatuA7KA9cBsROf5kdPj7LOD59h7hDniPmAo8DnwJu5gbHZ1NfUN5gJgLvDUniSvDcBuQGhUnvPl+Qp3cf4xwPFxhxKpTHZFvOblv/J9vba1AnUVWlWMS5DCPVBNYUTH9T5wjc3l/SOQn/sxgM3lNQMfACOAmObiI0Qb6aj9/zLind9hc3nfCuTn/qupuT/S7hnjVJvLawzk50Z25+FsLu9RwOxAfm7F7py/tzg9zueNEfXEDyXayfXiToI6lfYwYLqjwL83g/J+iHYV4zY04e/0OM0IoVF3T6fHOQz4su6IaoxQ74dyFnA7e94mTIAr7tAtiE4/foD0n+2kkYnv11ftyYVOj7MdkrQopcM7adVrL1KjodarJSnSUalt2RLRn5yonfpSID93rM3lvQ94DlEG+2LySQO6xx3aaVpOj9OAKLP+wChfnq/M6XEOBOyxc5Ta7JPEXhTJWHET+yiU92m0oen7z9lBeoMRI+8NwGMIFUQdqgLW2tJ4ddPpgBl4Q6vsbPMbwIvAIsAErEF0Qj9qs0QQwj8fOBW4tK+07Jhic307VJFNwBRgHkKwbIXT4zQCvyPsFuudHuep2z7uNv/XvTuby3uTzeUtsLm8X9lc3mbbpr0fiM0S/gdcCKwAOgAv7HYKbqsTt/U73NavcFvTd3LW3YhZ1fHAa8BLADaXNwm4TjvnD207ThsE1WFzeSWby+u23fOF6nhpdHHvt48o6vX2EdN6v9evi3bK64iZiatVJPIubusA3Nb/qqPbzgqbxExZU3HGdwo7zLfWIZy4o98kOdIjrbsbyVg6K5Cf+yHwiPbTqzaXt5W2f2sGlaOvNUxq1U1aa9OO/YyoI4uBRwL5uTciVOAgBlMghIdVOyd+BvrO7jyfzeXNRmi3NthcXtsuTt9rnB5nH+CmvitVu6zWDQih/n0+iFA7L/DbHZ324hZDte0abXuuzeU9Q9vvytYmCAPw6taXy9tOep60ubx372EeBgEt4/4/xelx3o3QYtTdaA/TbEjiNYh7qgW4SZLUNIDkdh9KaV2f6pja5VmSWo8npeOr7rTuDwxJav2ZKpm2vAoQyM/diJABsG/PfDJbC+Kdtb88IALcitDIxWTdLfHnSajISasXpHbNJ63buFG93+v7+j7kbZ8LczQQ34HH0otV5ima3fiBb6JHzr0rfBUAS8uTSa+pH3B3W6eCWleedepITUCP0v6dZI1G60ehgiTgWJvL2x+4Szv2PcBphj9ZY6pvE9vUlhs01VTsPhLwJHCEdigFYeOI/W5HjHTjkQFsLu8pwPOImfQZwDSby/uSzeVtzn7A6XGmU28m+NWX5ysHLgYU4BKnx3nuTi+O4bamId7byYhn+N8OzhkIPKz9NwlAVbmq4sGWWaA+j1BzbgBOQMzODMD4OAEBwib0YFKrrzGm+7MkY1Uz2Vg1RI2k/uz0OLsDebETS2T55KAszZptsUzr917vj50e52tOj/MwAJvLO8jm+vY7VTHHbEyxWWCTEMpAO+rVzrDzfPdCaB8UhGBYCXRGzCKQ5CgpHd7qqLWTR4BZCBv7XTaXV+orLbspP/NOaPkDH1oeoqu0FuCvQH7utEB+bq9Afu4D2n0+0bYX21ze0dSX/32B/NwJwJi43+PV7lthc3kNNpf3VYSmKhkxc9qj2dMecjbAEUtE615ZL7pkTa16s/Z/D2CF3+54yG937MkMK6Yd+wihPpWAz7U2332bc/sjTHFViNk5qHWayEeBj2P7Npe33R7kITaw/RyYiKgrj5M4nt/xQnlP87FtnwqAKXMuhpRVSIZaTJmzpbSuT4+O+znWle/LM9+4zf/bpeX0ODsDb21z+ExNVowEUFWpFABJiaTYXquRTeXiX0m52ulxXu70OD+JmzTuNvtamCdt87+kVfoh2v9/AeAO1owN31TqjQqZN786BYDFbVKiIYOJ9BB0XptUql1zdlx6JwLNcyIR5qxc/dMPa9bff0JlFdmRKGZVXa+dczJiNm4BfgBOASbaLIuJSNu1v8FADUI9Gq/2vAwxGoL6Dqqv0+NM1tRH32jHQsD82LNq29u07W9ALUKtcQNCLbg/cGp5We/L820G8OX5pgPjtN/fdXqc2zqpbctFbO34cPNWM1S3tQPwJ0ClalnTJfRB9wKlPZuMBuNJbboWp3R89WqkMMDYdIcrPbXLE+8hV/mBVojZ0oc2l/d54BHJGMSUMReAYVtagmJANpXZgCUALSKRed1raqmRZV7MymRsy5wjo5J0AXANMKvnq7m5wE+SMXiyJNeaVFVSgJgD1IFXX7utnXFbT/Hef6Jsc3l72FzeW20u752aSjhG/LsmWVFMO0kt1vHPRNSr3r4830pfnu/NqlVX/KVGzciWLW2AN9MdLgW4Vzv/Bgu1o29KfbXNXa3T+ciazpR0iffN+YsD+blLdnCfL4FSoAswHmHWmoKwIxPIz/2E+nbxtOY0tiOuoV57UYsQ6o3pVzAou0zlKG3BwbzOIluqqAcD2HrCAPDAT+0HXmtzeX+zubx+m8v79C40W7H3/y/iuT5FDDa9taUD79zm3Fhf+DNQLHalmOp6JWLAuQbR5/5pc3mn2Vze3bHR9wFoG46seH3DpnzqZ4qfUT+BOZD1fivzkd/uSPXbHY5dDX6cHmdroIuqSlStugpVJYoY0JyuqoZAyxKVVsV1Vedep8fZT9uPmctOtbm8F+5pZv0P9M592vTqiGaUAWxCqiGp7QcnOD3Oa50eZ2rcqZcg2sG/iEkGiDI+HsgGQmpt9noAU+asoCQph6uqHI1U2cSLUNS306vU86kfGO42ey2UtYY5TPt3fZfStXzy3YP3g7olVGKMzZSnx11iV7TbVZRpfZBc+8dya9sVAKf80Sr9kp+jvPZi5Jo/BzqKj3mh99kGVR0JcGxVNSYgVVX5v01b+H3NOh7ZXLQGQFWlMyRD+UjtHvcF8nPVd0xPXuPLEKOWtLAYrKqSqgbyc/8GvtLOnWRzeZ2a1/JL2rEXgCsQjcrYZb3a764J0Zeyy9SuwCZExxhTZck2lzeTeoeBy9larTGS/UM/bTtvm+MPAT8BqcB3To/z0mPe7pmM23oHbusM3NbnlXszm/vtjntW/97sgWitBEI9HULY0boKD3nrQ4jZjiWiyouPqnk2M4rBPik6mKebZVKWVIYhZTWWlt98n2Z3mYENsrnkl7TuD3e2tJwURq4CoS69CejZL30ySNAnVMOr5f9wz5YyVY3rti8Llve+sEyU3fiMdMoNMt1qa2kTjpQCsqqaHgcy5KQNACi1Oaqq1s+UnR5nvDd2o+K3O9L9dsfZfrvjBMdtX4x65b6LXlFVCgDvejV7CVAA/B/wBPC75vcAW6sjqZblNx98qdN5bE97bbvGl+er8uX56hyYolXd2levuxBVFRoR4O0U2wu/Al8Dpv7y0k+nWesd4Menp9FSKu64xNmju9/uuM1vd8z02x0f+O2ONoH83Ip0Km+zUGfu+Qw4PpA0piVu6/O4rYMQ5qEQwpHlc5vLG29vjvUHsQ7oHsAayM+duPtvc8/w2x3SmdOiQ159OYoErM5ozj9dhOWrwmLIRtN6bUqzzH32+M5zlmS3WADQrmLTC5KqHI1oy7cBBTaXd5TN5bXs4DZ1QlmzpV8LojeXTUWDtzm3rbb9kzrBUS+UtetjmqCOZsJDiHOs+w8GAtxdVDx2SCg05ZWNm85FOAmeB2zUzjkgM2W/3dGSeu0iCBPfRoTZ4yft951xPIBSm0O0qssCkI8ABvvyfJMef7/m2GffiIaefz1ae2SB8gdCOL6naYRiQrk18KHN5d22HHaO2yrZpTWTzjFMJd/0ZjlS7eoU22uYMhb9D2F6uCvu7Nhg4ylfnu8SYAbCdDpZO/6jEk23AhiS12QDKDUtPqzZJHz/Tpup8sJr0fCDH0U373b+NPalMDsAOUjhyNHZryjj5j5LZm1lG5CabVmcngFUA/OdHqfUdZy7JdBaBb5JSyGpRNTVvq1LDj+207xCgBGrlxtO/UelWQVkV5DVtkj9XNHslP1DNSAE5hC0RnFCZdVhRE1lkqQ2T+n8HIbUfxVgIcAA04IBE9OFiclRIjRFcf3+VYgZvMXScuIjiBlAMkKo3elbubqmWTRaCHD75PBHA5arV1/znQIw2ZfnW8XW6pORiFHqskB+bgBhZ30dqQaksMnm8u5sBtQg9HFNuEiqbnkRwBWlweNwW2+J/ebL84URNpB5QAvg3VqJ1WuMxicRdqqbipakTgceq9yY1Lp0eSoIB4X5ANGwNGjLorSnVIWYirPq4cgl07aQmQ7Mn5OUdMovqSl1eTFlzhwhSXyMqLjVkoTF3Gy6Ka3rU4op8++pCPUbWamL1JxSlas+o/Lfr1oyeBLSCfP6F4ZLB3BcqVE9q7zC2KNa8sU/5/XFQeXeouJMgGTzhj5HZXlIae8BQAm1NagRa2wW+hEQvPvlzssVt/Vm3NZTGuI97wi/3dEpLBmWac/142u/Pv31SdP/vm7dtCzTlkVpnBj9pyvCG/V37ZKewOM2l3dZaMPp47dNb7XROG7bY9QL5a2W+Gg223bRyh6okfSrtPtcakhe/7ul1RePAJsHSgX8llJfPv9azLxU3Tw1Gpb8CB+Pw4GLUluHJuK2futLuuq5+ZarQv2kZYuA/wWSxkiIdnIT8HogP3c5oj1GEGrHj7ep3x0RQiyMcMxp1CVx67M5/4I/VDEAk1UUh4naMqEQqrYomautVhfAb4eFD/vr8NX9nz23rE/IKOMoWW30FN4Z+cX8vxkXGH5ZZ6E2BzGQCdpc3rpO2ebyplLvDPcvQCA/Nwgc0bqZ921j6koAlEhaTGMX4zu2V7EGtO3vwOpR8jQWWS7nBsPEYTaXt9/OntHpcXYDukqqqvYP1SQDluHVoVHl/nxnuT9/cWXguge1UzM0ter+Zkd1NmbbPw7YuMje8zjc1jTc1k64rSao86m4FyBS3gtg/MJL58/25fkWAHRbzx1GhSQJzLd+pRzWrEIJAn37hmomplC5rcxysft0kCRRJkPlhUZzs2nNDdrgXuNU3NZWqx/NvgVVja1U+FnbfqttDVnlKu028SbRpK1MlLKp9EmlumOJYeUozvtDUVJrMPVare5xO9gXoXw4qKS3f7N6vWFVu/RgfZ2o3GhBVfjH2anD2cCq5DZfbExq/RlS1ozSe3Ka026LqLOtraG0rC5Vg82tI2GAtemZdTMB+xoVVZJkAH+oz5SPfznqbv+nbfr6J7TuDyw1gfmoNQPToqFWyMZKUjq8Ix9tu2XVYe/1/uKB5s1cxQYDqVG1tm1FTDsl7ql5g35jSPNjyvo75kT2PnCkb+XqLOCv3IpKR/e1Ktmb5E4A/VaqfPB0+Fy/3THPWqEaAFTF8AxEY2qNb7S0VeB6U+Y/1Wk9HkxO6fx0o60pvO/eW8Z8Z7nrg5bGDYMBDgvVWIBncVvrbJW+PF8ZYib/NUCZwdD8thbNCcryRCVKuHRZap3nZDCQrNZWGBYgvLcpnG3N3+zLuK2oII0Zin08kPZ+9MRe2umvLerwe6uwJNGpNky/UI0iScQkQBQx0x4JLJQM1XJS64lHpjtcE46W5x1VLFdx/ydRWq41pEZrDFRvtnDNz39nDljUY9NzJSukZEVVr696WFXV+vr0VtkNco9qGUlVCZuqmNPKX/ebUt0WU216F+L4Ni218xPNsp4jzi+godmcbP3KpEZbANQajbSoLqV2vYnytcls9mWgTlU4jtlXBvJzj0EINoA7gC5JxtLtZmWFRkPHWKcVR91MeZvjgwBSqV66aO2iwDllFeNkVa0EhqZZZ077JfVSjkn7li1GAyaFGuBGSVHp97sZCUkOmSgCNQgQrZEPB3KB9CQpnDTR8kBqIGlMlXYsZvvsh9vaO5Cf+x71ntynAN9qTn5Qv9xqbiA/t3zP3uaekyQrjwH81Uelx9kbGGhbRt9llhsAWpSpdAgG29Qa4I/eoh4VZ0X4WptTBYok440dpCNat/i87SdpN1XlUFIMWCRVedzm8sbq+GXathDqVlYSSBpjsmf8dDlAuPgIlFBsggzAdxPGRazv/V/klV6rFCRDiKS2H6sjs18pwm19LpA0Zt4DRs+tL5hfxiRFucM0AQPRn20u7w6d+dA0b4OrQ0q6pk4KVLc4q2fRyvdRVbsaSYvZ+mXgY80xcL/gtztORHtHprRIrB+kNEvh86H1bVeWlJ83zs4oj4alFcDfuK0Sou70UKMWpbboaIC/49JNR2h+AJAgPf/PSivA/CTLyUe1eDnevAkwyubynslusExpU/eeU6Wa5OT0ec0BwsG+c7XDhxUaDH9NyEh7FkkyoKqLfHm+gPbbzwBtt6i8+GpUHfcuT171+wbLQx9EYmr2MskQKgD+HrpYxRRFRkxw3t2dvMWzV0JZOEmptxhSl0Lq6vR+K0SFWdIOZLOCEpZ5q6R7GsK5oT2AKXM2hlaTM5NDKs21JitLnCbJrO48fJNJPd7Adce4kkvNqTcBONaINM2KypuVVx6VFSqfAbyKIr2pqNI7AOdG/HJV4IbFFkUtBpidnNQiIkln/ZqaMgKgV7U8WxIyFEWSJG1JD6D4LTk/opmc3zq7rOIK38rVdwDLgUGXl5ZxxoytV0xZwlIK0HfoYtUOIMnRDHPOj0ZQf6Pe85VAfq5iTPevlSSFaFWnrZaBNRhuq3yp4YeXVqRUsdEkkx5VGBCqixkRwG2NmRXw5flKfXm+M14s3Hy9NRrFbzEzrEPb7gvXpc+OhAxIRgVQqQmapOXftnQAs6q2mAgGUtoCbF6Qwfm19+V2qv7w4pMCM4YMKCwA8KL5DQysqln57MbNcv9QKOa5l+/L89X48nw/INRs4xGz5wmru3z0Sc+FBqlVKSgShVZb1a1JzWpRI7LFNfPD9JoyAwvUztIqtVWfzFX9OWGOglTapfjvaP+FY2oeYnBVmMwKlVNnKDzzdTWvfl3JzE1v4TQsr5sSJtVkAvCxNZ0v07TpTAPzybDT7DnVwb6g0iW3EOcZa2g1sJRI7yo+P8oQrTVA9RYLT61+rS1Aatf8902ZM2aYs3/HkLKCw0xztwvSscZkknPbtf5VcyqMsUOhfLQ87+RA0hgWJV3RDfjlwaLi+75duz61S20ttbJkeSE7I+e7NPFKLChf+/J8L1/7W2RciyBUJME1N8nNMkcWfQsQKjURrZVcCHvwBoQK8iLgUhDT3m/SUlhgMT8AEMjP/QuhgakCTjQ3/+XV3u85x6d0ev5Vc/OfAWVGA7zi/+SPQQ5LaqkYMC/qnoMqy+RIQa4rmVwX60ABPjpGZoOhp8+i8F27cEQxthTq+U6FKmtNJt7MtHJFO2vK2BzXJ+cs/W3W15Pulh/+663f3jh2dBL1zkCPx9vF1xkNN85OskgAtwQ3ANHs2G8tS9QXgE9SajjuwY8VWhepmDIWSJkp/m8Qqv0+lxt/+CL+WTpLG7LZgf+Jtg75coArgmViIhCFmt8Mg56Z+jJjlvzENqbk8xGayb3xMt9jjCmRhwCyulaSfURFnQr7i4FGJgyXuTvPwO9OCVSJkqVpLP+2BSt/aN6/YELrdYf/q1wNEA4OkFEssLXp7VwgVZLVVW0Gl6gAzRZauHO5WNk5O7OkD3JdXxeLsHgbO8Hm8tpiTr2FamadiWiDwYCSXJimqhI1m045zBzKqgAYn5Fmm6BpWe8sLl0Yl9QsgFEzFMxRJIsScZy8cAuOteIY8IQvz6ek1VbNOWNFXXgIj6PAv8c+FXsslF+7/PAFH/56e/TFmXcM7Wl+mxPmKFzyqxBgs7rKlLYXfXP1pmh/RK3x1JYOLI5dP3iVOFc2KeXJL275FjhKkqFn8zW0kEulV/ucGQSwr4XMCpWsiIGOxZuxlW/srSVxzL1ll9kA+srLyVBrr7+xpHTuUVXV3FRcSrtwWKSvqlgKj/HE5/3qVjkTAFK7PHWCIWkDqmJCiaS53EXFDyAEawqwqGhVq+P6LRMVfvxwmRVx/sPd16udY/uW5lNIs9+tpDtcWy3jMiSvMgFIkvLhnr7fXfHCNcMfnzq5TbTIq2atmJ5NarVKbmUlafWG2RbAD9suJTq6qnrEqxs3k6wo1UhSz7/K0zsAWDtWk96uTsOSt3FOxqJVv9ZrZRQkLJFw2uANizy3zPuMh/96m8kTb+9nrVD7X/V9lLNeT2oT9GZH3lu7yfjyxk3XIpahAODL80WAizqGw+8YVFXZbDS06asN4GSVx9ocWfp8+xHFMw2WKMaQkrziu5b414rJ2d3eVUVX/aDw6atLMiZPvH3DU5NenjLmw5QFz78ZiVzyq0L7xSayF1vYODmbU78yctsXUY4oUNi84o7ZtcVi0vZA8+btG0Ot12/LMj/ApjYqE9sk82e6hWW9o+Sdms6EIZJhcQdxy2iNfIbT4zxeNpUuS2r91RGWFt+T1v4tJdlcuMM8rTaZhgF/Oj1OK4Ckqh20n7YSytcaJ8XPFtYBc9pHoqVPbC7CoKr8mprC+Azh41Qhy58CHDNTygH4xwHVZlla1NpwocESBUXixkjbvs5OHS4/sX2bminJSQBXArlVksQZ7VovvienOde3zDnnyHd7ZQIE8nO9wA2mzL+x5Px0qSQx2pC0IdOS8zMpnZ/r5/Q4u2lLDBsHAyFLGErSYKoyZomM4gdIaxs6PHbKnz1SPprY6dinQoW5A2etWj1t8tr18t3RzUsAmpeBbaN6ZZtwZHlEkvjKnH5Nnt9rN6lRDi8syPmnpaMK4XldQfxMx21Nm2+xjFYlia61tVzLn5hTVtRpPV58LdoGYdYD4Lj5Yuw1O8kyYmc9c08pANBZW1MezxlAUnYkuv7wUA1/RXtStjYJpUxUnQuW/Bw0KNstGe9lrE5+xm93nOO3O0b47Y7Dtz2hIfhwdN/SSJXxSIDMrpUkSeE6T/R/O6orTguqa5e3kXjlVAOzhlunlplTiNYYCJWYURWp9ai/lRMBIhV2EOFLhXxwW2VQLwPI7FJZmtG+WkrKqq1QwjIDP0upvX1SBCmsGI2pdb6KDyHGX0NtLm9vtsHm8p4FrGxG2XO4rScPNSw+DiCqStEfNdNbtMqGIZLK2OCmNIA3M61UyzLtqyJcWFp+Jm6rBcC3cnXUtaW4sN/K7UvyiAKVF16NPA3wwIx327Wr2EzIYlK6nbFxFG7riO0u2AV7LJRNESU1uwy6rocn341y1Q8KBkVicYd0vh8g8U1fMTMdUlCNKWSiYuldRTUbzmoW2ngaLYoM3mu+UxQANSoJu5o7GEBEguIow3wO67h8VHJ2LbIKh/+rMGJ+ZvC5P17cKg/rtzS7pkhNJ1OqZEHSVbmXlpX3fKlwM1cFyx59feNmjq+s4pXCzXO+qz3xi7XR9nXXzbNYcp0e58myueR6AGnLUC5a0bcTwjEF4MbnImc5f1w94CGjorKsFXwxTMZ1mZF2R2/xAxy+XFFkRSVa3RZVRZEkjkOsjwbA6XFaJTliAzBlzt5j1cWuiBpJaR6EjEqJI5aoPO6JckKwijI1ZSz1LvwpxAc2ECqjY5y1tVxVWnYjQNvNwgM4JacWa6c6q8GNJf+mPYwSp35CZcS6+aELC36s+x+Y9OaL0f4nzFUxKFhqy0zGyk0WRlSHHL4831YqBt/K1Wd8u3bDxd+vWS9fuzFIj3V1lXoq7qBqTFLy2g4pqZYMogPrOXNl6VW+SX93LtuYDSAJJ48TkqO1R7UoDvVJDkmxzv5pJPUvVZHoE1A54l+Vm79WQlcs/O4iZdPwzcZQMkp1+82IWXqDEtVez/j+Rh5p3owbWrXg8tYtCQvVy5aV2iBuQ9jUX1LVr4DmLSLCRKPIijxXm2kptVkRgKRQZl3v2rpIzb57fPTlcVc5HlElqbVBVXEVFVfaXF6LzeU9PPJg1nlHygVtANYoOT8ANtzBAUCzHrXh9h1qIrdQH1mpivp140MAwi0iywF+S01mVQdRFj3WqBcAR2wwGm03tmrBX0kW51KTyXhau9Y1q02mngBBg0E6qqr68Vg+U7s+NtXSapIKUFsyiNriobWqimqwbBqOsMFucHqcdQKqIanWxOCyThZ1k9LhDEkSATvS21ffAmoVqEvzbKtu+bf8/Q9WGm7yIlZnYLCojyPW8PPku9HV9xcVj0pSFIb/LRmNSv065+HrF0gSCvcaP5wQSBrTN+7WrllJlkyAtqHorNLlKVy6xEhOqcpxc5V8tJUjimZD7rJeVJSNRiMrTcb5COekAUAztNlxbzkQQfTDHZ0eZy+nx/mV0+OcgTB1MLxCrZWAqYqTwmWZdY3HqCrWcxfPCasRM0mKWntsUcaTANf8GDoT4ag3Bfjbb3c0fBQ2RUkCWNoGQpnq0vXNoNYImzNgZY4h9/GStWc4QzVBgCeHVb53x7Dr/1nYrhMpOWKG23ETFqIyUeGp7MVtNeO2vldbbigHaRioanbPir6SDDnO8jzgD1TJPGgRjPldwZjuB3gzkJ+7Hs00h3D6Otzm8va0ubxna46HdzQnyNfm+8cibP1EVYkpSt+n/xaDT9QKu/qy6YWpF1VtwKEp+1oXqTz+usrKyS3MgV+yH/XbHR+v+D7n/wZ/ntQyK84ws6FrlOI0SA9Bq1J+9dsdcs/iwGCAtD41sjFJOZqtI/ftFnsslIusGbcsHmH8VUqt73vT2lZPHX90j2FRE0zqbqY4Q1VTa2DQvLaokaz/gUxSYb9/X3qjpqsUkmUkdZGqSLfEJesFGGP4hauM3jPT2oqZW+78Zhz7Z3VdhzWvuViOe1jhMv5Sev6lHb4DUdlrgHEdIpFbnt205fuh1aELA/m5W36NDmrpWVt8a5KiUC3LZrTCOa+snBfL5jNEXnRZdbHJsPbPrC3+T9v8b8jX80uPXzN7GMDP3cSkOBpq/VtyZmSUbFYw1UpyzxlnUxUYG0KxxAJm9Ix7ltjyo0BsiVJDUmMyv/rziKz3PjrePD9sQG1dAgVLhjCo5pUNuINXxZ4PYROM0QPh8Ru6Klj2cVq1OreTFgNINqoXpLWpuRfUCQgBeBLAs6fLfHS0qB63zp2Q1LlMOET82VMiotWaLemoquYEU74uCeAo3FYxKnNbO+C2PoKw15vS1hj/OM6TssYiFBnLgAXivGBBasvaod1OL5wgGZQNpkg086zlf8Tsk3MQwWDy0NbmanzlKPDf4fAXDAHuikpSAYBRIemcZVP++mLik5nvP1fJ457wEF+eb1/Dq27Hr0c1e8uTm/XCH72kmHF7LWLEXggcUZgp8rOmLElWJSntsFBInbxmvamfZmKolsULjFbbrqpY6gqHVt5ofH7jZvI2lPPY+1EOW6FemDud+9pvVrk8WMb5wcpTAW8zymbWYPpUVeGHTf15yXfaVbiDEb/dYfV/2uZG/6dtfnniWemuWyZGr0irUq8HnL48X5Hf7shCq6OrsuRxAJPS03h3gJicHeNTI4ctU+5Gc/J7M9PKNa1y2GQ0WoA1Z8+vLbnp6ygV5cbRINb1y6byhyRJkaLV7UprNp45t6bwtB6SxEnAn223qDzwcbT5hb9GY6saGpTFPXMe+fGY7BdXt2+eEcjPLUCsA55ntKjNu5+1ManHORtbJWWFNyPqWMyLtgzhlDdF+/+sYXcVLh6wUll20hwh694+UZTLgMKC6B/KzZOvMn53OfAbbuu5uK1ZwK0ztM78VK+hZMM/mYycWMXLr0a55nvFhdbmZM2XocNGM7aQWg1wVasWLZ2dOtzn7NRhGe5gCZrK9ghZRBozNZtyEqK+nwEMklS1G6rKyMqqHIDicJo3ukUIeUtmeCHAJf6fTe+/GObLr8rMjwaW3Nlms5GjFofr3lPIYOKBI69o8I/KF7ZsdtPdeQYeGmPgnqThEW92Ctdfb+Cuyw0/+fJ8BbiDs31JltiEpM/qjFYX3jHwBhaP6IQqqySFIWdTDqjmCYhANXcAeZWbLCkAKTm1kilZAfghrU3NV8DR5gwR5OLoBSrNjHMqU7uOizmg3owIL9sXsXRwEaKcrzMS6feC6UXay5spUjPUadFejIuMUZ609M+flWRRAT6NeAInGWYNNwIDNzinRCo7h0dNM2MJSdSWG6nebLkduKCm1HRLTYloL1Km+nPWyZUcNaAQuQVva/kYClxmUNXuAJ3bF1KpWtRjap6JecjvNnsslO988bdvzm6z+viuJ26e13pQCe2GFRW3HVx6zoR7PpjWs6Z2sSpJ/NjPIAGcMXfTWoTA/e7pqS8vQwiHCKo0xlHgjw9u/66qEnbKAdKkkGxqHS0BaLOxiOxQeTbAo4df8uT47scqAMetmVV72Iwl1yDsld8BE4A83MEq3MHncQdP9n/aZqPf7sgK5Odu6h+ueOPc8oq6UYRBVbm+JMjRhvn0KlpxceCn5pSvTW4OdE4PV2cAVJiSyqelnPdc1aorqVp1tbMrH65Ozq4tBhi1cQ7AVMlQM09LMhYfGDQnHEQFaXBcz/2yZOwb0y979KX5/UxREcxDXqUSwhxTaU7UtvHrB4/Wtn/hDobOnar8IquwyYoyZHiLL6WHgo+DdAmajabWQGh2N4nvBkpsjltgNN8m8cLpBq682cBdlxq49WrDRAnGAgQDyYSr5X5ABLd1BWIZ1X1A8qb56YvX/NFsqKpI7RHq1oscBf76zsIdnGt4vPQ8NSofjijPYkSwhuMcBX6vo8D/vqPA/yZiTfoP1AcxwVHgf7K3f7EDMSItAjJNatRkVBVUSVJtLm+DO7/c9Nq0q/KfmX6zJEu9gWa+PF97xIy8jS/Pt8KocL0CdN4IOaUqdxWVSmaYudZodMenY7LO/VGNZPYqJ432lc04+R+VNM2SYIrCzV9HuXBLBcvUtmcCx11j/IYUalg8ux0dft3Ipf7vZ/jtjm8QtuAXEN7PrYf41ffeeT761IRxkZjqLBZAZvlPncx1JpWFNolgCgXmCMa7P1NuHLBUuUNWVP5JTmKz0YhBVQuefSNy6+jvpIxhi1Uu/8SQ/eNQx9kIR5wLAQzJa08L5J/aP5CfG/Dl+X7yrVx9431/Vtf0XqVy+gz1NL/dsZ1acV+59ZU/Hrj51T9vuu//fhYhPN3BmOPZFINZlWWjuqOIdE/gDlZRv956tN/uaHX5N2TJKvzRS+KHATIbs1VkMKRvqD5ZswgZEf1L8XqjIWW1yYQprEQ7BKT+O7gHCMH6aVSS1fRwiLM3VRgBNhmNrRDxC27XzvsJoLe0MqUFJZgyZ18JmG/4Jlr2xguRyJvPR3nxtWjFkdWlqYoqkb0sOANVkgxJUTocs2U5Ys00yaEwFf+mUjg9i7Nn12eioFl7XMOulf9p5aj3imwgbnt5yhtLrF2m15okfk1NdryV0oGyVImKZCleMzhL2x4fyM9dChTcFb2GYJZQtR23Mai0pPi6QNKYZDQP6soNlhKA5JxaEEtQz8AdVB0FfjWjfehtc0ZYMUfBvk5NlU3B+3BbjwwkjbnPZfz4fOCbbtJaxpsf5hXTc2RQ8fKTpjeShhgWU6laGF17v3Rh+F7eiua+ta79z2eHZFnKikbpUxuK2eAfvtM94Wir/+wzBy8RXZMxKYpkUKIASc1qI2ltQrQ8LOg+ZcSTj09N7YtBgiMPX1+OmBCCpqmUU5Qig1nlF6W/tFJtHR9pbrfYO+9rd1A1WpTTMztXP5Lermag/GjpJoAFSZYLAKb2EiO6DqVlbSdPvP2OyRNv/7xD+aYTtKvvcRT4F2yT3kZJqnd2+DJ9RJYveyt/hQ8/+uDuu/zNOrYOGUwzjapiLlud8jDu4Pm4g7n+T9us93/axuO3O7712x3n++2OGxCj/oV+uyMFd7AqJxq90qi1srxgGc0UBVWFivlJ6agSy7LaqI8MyuOeIVdz1XF3vpEWDrXdkpx9X7SqawQluTnQy5we+QPAXrQaxPKh2HKI1rGvw3QMh88CaBGJFOzVu90zPgDot2kp2dWlZ9pc3pxN89L/rSw0AxypReqC+q+r/A5w0mzVCTCjhySjrTV3FPhrHAX+a4CT3Oelra01SVSWD9v8wihDqFRzo/rLIZUAmVVJ0qCVraVeNWbpXETnMkONyGxZWNcXdkK4u/+2+vdmHxX503tqazbHA05HgX+HzkCOAv86R4E/11Hgz3YU+K90FPhLt/n9e0eBf6SjwD9vB9fOQwjma4Ejfmk/YIC9ZPUxO4px3lD48nyKL89XErevADzz/KLfQmYROOfGX8OLetXWDgKGDJgvjXeuVOKj123QOqwHp9X2wvivmIW9frJMaQp02AybPmuJvCByeA4l5Ck/RNZObYa8rG480xqhSUhGrA29m/qQmanAO36743zq4y2/4cvz1SAidlWpknSOtYoTEOrWtnd9rvzoeSaqtt8k8nfPJ8obbYuYIGnrbVNqoH0Rnx+2TInZk+7b6qMnbmuPmqDxt+ylRguICFsbLi/ZrS+67TPu4AbcwaMRNl0Hwi6+CKEmnoAI0wsijO5yhAp5Q3o12RVJVE45Ur0eYKpDdIkb/skk8HPzxYhofQDMSBLlc9x8VkpI2YakaNg+ej0vnnk2f7Ttuy6KVARc5jh/vbk4NT0CcMbqKpOrqHg9qhoTjpc4PU4Jd3AV8LcswfCk35DNm/oe/q/CUQvVjMxKjBnV0LKUtFCRmRVqa/W4JbMiACnNazFa1MMc568fE04zvG7JFDPjcIWR4XPFiO6ZUzOIHGfk41aPLw0kjalfF9eASMbyW1RVwpSxCNUcRFWlEFqkPw0vIoBML6fH6QT+KiOVxc1TJIDhm7bIM5Ju/ALtIxOqyqLytUlBECY1YJw22AIg54u1qiUjUgjQZYOKpKrXzbZY/gKuvtb47YOBpDEXfGu+968j5AJOMczkPuNHnGUQVXOmYr99udr2IeDylE7P3Ylmzji/rKJUFpOH89G0G2/8/GR6SqSGzclWOo3arNjP3WhwnL+eTiduMbYfUexv1qMyX5ENvX5WhEJUkhiV3LwmFrQHUGvaDSoOARSqWW+yv7yvAXAHV+MOPoA7WOfhqq0zG745U5pYlM5cSTh6LUbEzDUBEx0F/p0FZL8jqKa87VM6Rd6NjmROi62+//AFwJKnz9qUFA1fjlgreYbf7jjTb3c8i1g6YEGojz5BBAPpgIic1A/gslvXvnf/luKTv1+zLnprSVDxKZ1+KpybQXWRUEm87DxLmt7Gyf+3d57hUVRtA75ntqRnSYAAoS1NMsAiTbEi2DV2fO0ae+819rUSe/e1fhorYvdlVVAUUBQEaQvM0hcCoZNsetmd+X6c2WQJoScQ5dzXxcXulDOzJzPnOeepszIOeHZVSsZtWkAvtUrVRdVdg1S7+QVAQnEVbYziuQh1pYGYTbeveqTVWRWKchDArZuL+1i23GZDC+hLTZhiw2T4qlnxHUvXn70pkPLZyl/bUFoYZwdOwus6A5EZLQJ8pWdpbVRrFf3TQBXECqOOc+611yzuUtUDoLZ48O8LOyv333ytjTuusPHrgcrr/hx/yJ/jn+7P8S/w5/gjlnfh3QDFSxONTQuTzkDEKLbXR2f+r3xtfDTrzoPA+VpAL2rG/ijQAvqbWkD/68afPpqpBfT5Oz6reUis4WkAbaHaUx+dOQ9vKHLFeOORB0cbnDBTpJUdMyrc1kqw8N3CVR0xalWUpAh/9zN4+UylxFDMCoBwQLW9NvcFs+CH1vaywmgEEncivKRvRWhn+mkBPU8L6Cch8shPQLx/nyJSb65AJPPHn+N/AUj25/i/1AL6KkR++Y0AcWGUu76McNJ0Y7JnhfkEYoz4cOpF5aHo5KxPgZmCSIxS50uB19UtXK3+UjA5PY2wyoYMw9RPr1heriitmqF7t403VIA3FMAbehdvqB/e0I14Q+fiDZUCaAE9gui3qPNpTXIVp39yl/5fYP0vB6qYVvhk1SZnH3105pOIQbvqz4R4A2DEHLF6SmpfPUNR4RT7NEYddHHHU8545htrwTG2Z5s1DoDazU4uLCl7FEUZjCg925V6bdpnAK2MaTz0aYS7vqybbM2OfihfG0conDgnIVJzHkBC65oIYmzL6n9KwaLuJ25AGVA/zKxJw5zRp5z0lJm0Vkp7As0ilBdc/ct0M5zyXwDTJKIo5r3+HH+d9tOf4y+m3pT2thq/ah6Y6J3FHLltoYppMtw0eR2gdFX8T6C4wayuLbN1wRt6seE1bfHGXwAHr4pgKop6bfu2rLHZQPhLlMUptXWJRM6xi2E7YipfjHhs8nPBvGxvMC/7PVv82pEIU97S9TZbJtANb+izaBlhhxm5CODnzkMYYwxv6NXlxRuqBvr9ZngIm2oE6N7xsKJnUcShyZnVjyS1r+kIVF1l//72xsqh7ogmzwTjz/H/7s/xn9m6lNOoT0m5GaGi2HYeZm8o7HpkzZWn1jzRaaXZ7sLiuOSTEUJvNTA+epg10D5vff2K+ixaP1KfbjGW1/UsbZmepV141p2FP3YMR4YAA1f90OrvokViIbnG1ZpAWlcDyArmZd8VzMuOLf0WneH2/Np+xHeoJmatyuiSxw6xEnQsBmgfDh/8Y1LiuxvsdlIiBsdUVJ5Nfd7uZkOBfIBjV87gpODUC7CyRYWWJYKYDH0GUFupvqyPzvRg5aUuTWD52nQF4Bwr5R2efM/ZpsnXioJSUzwEozpzmj/H/3xVnHJkQYZys6kojzd2D1pAnwyMB0VdP8t1EN7QL/rozPOo/zs9ogX0x3cnPOAfzLcITUoccKiepXVG5IrnivEG7z8fqUXYotd8/82d486dKQrLxLePLP5g7fqz53WxtVZNJU3JMGsAUpZVKOFKG6hmCDhbC+jPaQH9Yy2gv6QF9OmxfasF9FpEoo85CA/i34FzYisl+XP8scfPRWg3zgKM9sVw2c/GMMQK/BfgsrMjJX9POlyML1kiXPFuf44/gtd1OV7X18CydTNTM2vL7aCYK/p2KD3wjpLinqfetWZRU3fsnqIF9KkI4Xgs0EsL6NGqPos3pSqMOsd2HfUqyfv00Znz1y5JOmBcUuImW8SkywYRqmazmy8BnGz7i/HOu3jfkXc+XtdhwLCE1qKryzc4q4BP/Tn+SupXktFczp9uXhVfeerXETwr6v4cS5entj98ap8+fwFsXphE8tgyGyLdZklq18polsRjsMLWPu12NJH2Yihf3DeyLGJTsByZ/os31CxmNADVUXIDcJCi0N6f43+xkUPuRzx/QxPdr1/iaDWdOT0sxdVaJ4HPMlkzrRXVJfaidTNdVjUxZXzax2saxuWL69nMzwG6rIWe1TVmlapyQWb7hRtsamUEeLJ1Wui4TpmLC+z1jv82xXyiQTPRRciH3puWV0aFMYCepR2L5RcwqdPA0CPhS2xBo91ohGy5DuFAB9CvgnjWmOl+AEeiQacjNtP5qE10HrY5mjd+HN7QblVIU7bIcdjEWPlP04GQFtB3quRbg/NTAFML6GUNtichHDeik4qRWkD/Kuaaz7J1UYVyQNMCeoGepQ004W8FlHf6ZvN1z6MwFHVSMC97eMN7cOf6bkGsML4AvKN/fXieK1RO5mGba1xdqk66tl3bT6ckJmSMLC37e77TOTgQ5+T6ouKa64pLnMBPeEPbSgzQJOhZWqsIynob5hZexqrTiBxwxlpb2Zo4Ni9MXlexPq4u5V1EUXlm8Nnr/j7mfw5FrU1HhKM+hlDhqJGq9lQEr9uEGdfPqsyyM/dxDmICsAaRWStqO3sCeHA/E8gA6Fna+wgnte8Q9t6s7Z4APD34/Hvf+/ihPBCpK8+t/aX4Vv3z1HCVDZvTmFFSEJ+dNX9hs6mE9SztEKI564X3dj8toC/H63q1stx2Q/B/7TDBUKCHdl5h90itMsEMK1RudrDqt9aIGnDKoVpAbzZh0Fx48j1fIUw9148ZFV5NvWcvFU4mXXqH/aislWbNox9HnAj/hQztvMKnaSROdt6mLth+CiOStCjpWkA3PPmeMxBpfgsA95hR4aEm5hTFypig2Ez+dve+6QHPVad8bT5wQryvEjNct26qAk7Uzis8iZh0kKZJ8XE1z7z3nO318j61Kx6Y2DrOuL1dW7VnTQ1fr17bA29oWTN01U7jyfcMRWht6vJKP5KvRLTC2hhfD7MWlDUIDcDtWkB/obG29CzNCWY5KPZfT6y99r8DE14C4mymOT3BNDeVqeqJAJ2qnKs/KVzbIWB0eerQx/6MRtZEKwFuQmiOhvpz/H/FtN0KkRGyI/DWSWc82w6hQXospnBLNJ1sEeC62jb2pPscnwxFZPu6kPr6CQBH4w39uus91sw5U7WAbmoBfdPuCGTr/NKGAtnaXo5YhUYQAdpfxewztYB+BzHFxi2SqBcUIxRQ1ie0mvNlz+EYInFYo6tArBAKxEqizx9pwm+lfE280wgz4aSC6gzFNPk+KXFwIM6Japo1/aprTkLYtM7eRptNhhbQiysc8dFBFFOEwISMGtW21JfxZ8Fvrc1YgfxHh77GLUfdzKROB7erWHlF1GbjQDhPqeFSrbAieAOYca/urEC2+A6x8utAfT8/zn4qkC2ipQ5Poz5veleENuMYRF7jujjGkDORiZ0GuWPOz/rMMSL1hYHn1LY6svLszB9XHNScAhnqVpFtEaucU7WAHjVPzU9IihCfXrNZARXMCct/avP9ku/aseR/7Vj1W7q1slSe/ScKZItotESGFtC/I6bEa2INR/1ncoTDdGOTtekny1nxLuDIryOHb1HT+PeU/lTY4wxQXNSPRT8CpUDnIYuME4FPFBRl2gEKb1yrkvWfNawd0OaV7krhCQMSltNmUN3QV41YeEyiPsQNAEXhtp9HXX37gfbl7zgSDGNwVbUKsMTpxNOtS0lTdczu4s/xT0MUAloc3fa/gx2vIxy5LBQH9fHdW/y+WISmR/kbYMSPjmqsVW1EUQ4qU9UTM4pMBi82WO2s7ji8W9phsQLZwoMQyCHqHdGivIwQyEsQC7poKtzbrKIyURtBJ0RZ0/BbkVN+wRt6BG/ob4QpZypi8ffQ7gpk+OeUutsKLaD7EB101TYOmRjzOao2inokDwRoUxn6BkV5BLg+mJf9M42z2vq/I6BN6DwEgNDyRJaMbccBXyVz8QSjLsylTTHj2ryXvtSyae2Vl8IZEWnulrg6cs3Rd706u02PCQC15fZDFVBWJ7Xhp85DuGH4bTw29DJ1aatOiwCMSndm9frj68K2jNrUHytXXdzBWnTnN3atbaEF9CpEnOYcRMWnS7SAvj8L5Kha/2Xr6yLgWC2gr9QC+notoP+iBfS/tYD+G5Yj1thuh2MqyjnuXF+0oMswUBgTGTEl5ZG1XzZyiea6741aQH9SC+i/xGz+BaD9kFCKyC2ldK/a5IwzalVMQwGUOISpx7u37rMZiAqKtgBaQH8CMYi/DvCfKSYnzjSjkRY/AeANmXhDv99We8Nz/ave5t3wSRtmGj3/96ZxCr92GhgdO17Xs7QUf46/qm2x+eN/Jke4+TvjS8C9MYXKN05WCYXdiwHOsv3GPfbRBQAV7oS5iDE6XQvoUftsw3HqS+s+VgBPpBvGynbhcLG1b5cTVzQH/hy/HxGSdz5w0Qyt5g7EeBqHqEwW7ffl1Js8t8VP1v8j/Tn+CQiT6GJXufnnC29FNt/zhcEpf5kYijKikXOjoZbTok6ZAFYsd125W2vR9zki5XAywgx3pHV4NP3q7GBedn24pTe0Hm/oULyhZLyhugyPu0Ozqq/3NXqWdhXCE/NFhKOLgag9PA/hADFCC+gTt9eGO9fXDlH5xESons569dfn5/QIFdYlFShJoPTKW+0pHTaZPPtupMwRwQS6aQF90zaabVL0LE19/OBLZszI6D2w2h6H3Qhzz/SP/Z3K1nf9vtuhqWO7HYap1M2/1iDsU3cDd6HU4Gw9ETOS7K8tOjQBlJ7Ap8G87Au2cTnJLmLZk9drAb16W8eMHzKs97VH3zXRUNVo/rjTELHZpwDeYF72I3vhVreNcFqcChxcvs5ZWFYYn2nUKoQrbc+VrYkvQLxb+VpA3+ers93Fk++5GeFt/bk/x19Xw1fP0tRJ/ZRFR80zoznWTaCrFtDrbJ9Wxbg1iBrvbwLXJNRWffmV74FBCC3bncDzRUnMTisX5RgNhQ23Xm1LXpuuJHSvNg75cnXhRLti1HnyAYfhDf1JQ7yusxEpjD/EG7qikd/xGnA98LI/x7/LpQP3NnqW5kIslGbu6PnRs7TeCCdDE1Fv/EyEh/PzCIc8CtPhrwOUWWdMNXO0gF5X3MaT7/kaEQf+qD/H/3BMm9MQznfPaAG9rnKXO9eXivDUbwM8jfCLWoPQdJ0XzMveqrBMU/CvFsqx6FnaAoSAjjIbGLSjlZyVN7UKoeItAVIzyzaMfPfnpw5EFF64CWBDKgtbl9BLrdc+nKsF9DFN/DO2d59XUZ8LNpYKREKTzohwoYeCednzrQo/tyMe0kNiji8ADgzmZTebl7Skcdy5vkGI4iaZDXb1DuZl73uHKa/rTIRzZZQH8IYaOtL8Y7HKuH4KTPTn+EfEbE8FCrqvMVOvHxvJ77KRsVpA/6Lh+e5c3y/Ul3IFeOyHb+4sQ5TuXI9wSv0EoMIJz4xUme9WQag8Xf7lK9+mfiX2Md7QRdu8Wa8rFSiNdVSKud+of8ccf45/wM7+/n8KepY2i/qStSAmhFtpfU1YqsABlj2/N0KTowBH+3P8v1ptDQGmI8K3OmsBfQvzkDvXdx1CU/I9wrFuvWiapGBedmUT/zRo7If8i3mowfdnd0a1arm0L7W+pgIUJredqQX0h7WAfjNWScK2JfRWt+zPY5rgnneFD7DSlcZQAZwfzMsOBPOyfwrmZY8M5mXPBwjmZdcG87KfCuZlH4oYNEDYfY6XAnnfEMzLnomY/a+O2TypRQhkwTeIBBg+4JJ/k0C2qLMpN9h+OZC6rIOi33mV/fLGBLJFw0n4FwjzRcRq81WAgja8cekd9rAlkAE+tNLT3o8IwZzLlrV9t8YbKmlMIFtE7bL9PfmetO2288/k/xp8j3bkFmUcFaEajxbnOVdsYjJbmjajzlljGgpki+hKewgi+RXAyuYSyLAfCWXrRRpnfV3M1i/Q9ohNBFLBlvVtbwV+s9q8gvowqKv1LG2vqY6CednViMxd3RFx04cDvYJ52d9t7zzr3FyE7STLSlso2UcE87KLEQNANDFNs6Sq3C2E/fR1vKFT8IY+3PEJ/zi2sCkDWOUQb7K+vhRri2yE94FoyJI3mJc91/K1iJYGTAdqO2/kAYST0rvAWf4cv0jXG02A4g0diDe0mt3En+Nfi/DrULBynv/LeMP69yMizebHQI4W0J8CRtWqlK20/oKGwqXW3zBqjnsvGg6oZ2kHIuzcAI16fCNW0ZWISVX02GbNf9B8lVxaJiMRNrpfrVjOnWUOQs0L8EdsQLgW0FcT41Ah3PYJIkrgDWYvEszLDiOcJaB+cNjZc8t3fJRkbxDMy17rzvX1BrRgXvb0fX0/+xFRodzGk++xW1XOTkZMdIuwMuhti2BedpU71zcM6BTMy14Rs+s7xEQL4HstoG/yi9wL47dqpOm4EpHnYcmODvynEROH39i++07P6/dQQrW58eFPDZcJ56WWmxtKkpTeQDHwtZ6lqYhQzejKeowW0BvLcUEwL7vaneubiVjkXG9tbtbogv1KKFtedbtjnP8t5nNDFXHDa9ToWdowhDPV3O0dK5Fsi2Bedhlili7Ze2xAxOw7EHb9ldQnJ3rbn+Ov2MZ5dVhpXVc02Pw6wp8jk/pKbs3KFulP9zO+zZ0X7v9+v6/XteLSdsUk9Fhj3j2rpwJwvz/HH9JHaSOoF8hFbBlf3BjzEEI5yk/bOrAp2G/U13vIJER8IcQkFNgWVrpHX6x3pkQiadlYqumoaaqrJ98zADgaYRN+bVvn7QgrCqMfMFAL6GP39D4lO8ZUlLeCGSK0uF0xIEL63rB2e2IOPUgL6IVsn9hc/UHqk+s0C1Io7wSWWngAMDyYlz1rB4dLJJJ/LtFVbnesnO7AGH+Of+U2jt8ptIBe1FghFUmz8fc6y8WtfZEJcHKMP0A0CudJLaAv3frUrRgX8/nbYF52s4YsSaG8kwTzspcF87In7fhIiUTyD2aB9f/7CMceE5G2V/IPwp/jr1nZVqyU+60wq6zqaFG6W//vlL09mJddiIjeCbBth7AmY7+yKUskEskOaKgJe8mf42/UCUjSslmbptxsKLzcZQPxepb2gBbQo6mUo0J5+bbObUgwL/sxRH2AZkeulCUSiaSeWCeer6jP4y75h/Hl/fNeUc26le2jepbWVc/SbIj881Bf16BFIYWyRCKRWPhz/AXAfYj48HOtpB6Sfy7RohQKcCrCA96B8LLf7Vjw5mS/SbMpkUgkkv0PPUu7C5G72kCExJ4PLNUCes99emPbQK6UJRKJRPJv5jVETnmV+qxcLTapihTKEolEIvnXogX0CkRN69gayrP3zd3sGCmUJRKJRPKvxio+FFtA5fttHbuvkTZliUQikewX6FnakYCpBfQWm4ZUCmWJRCKRSFoIUn0tkUgkEkkLQQpliUQikUhaCFIoSyQSiUTSQpBCWSKRSCSSFoIUyhKJRCKRtBCkUJZIJBKJpIUghbJEIpFIJC0EKZQlEolEImkhSKEskUgkEkkLQQpliUQikUhaCFIoSyQSiUTSQpBCWSKRSCSSFoIUyhKJRCKRtBCkUJZIJBKJpIUghbJEIpFIJC0EKZQlEolEImkhSKEskUgkEkkLQQpliUQikUhaCFIoSyQSiUTSQpBCWSKRSCSSFoIUyhKJRCKRtBCkUJZIJBKJpIVgb9LWvK5LgZHAeXhD5XvSlDvX1wl4B3g5mJf9fRPcnUTyj0fP0jTgJcB70hnPHgEcCFwczMs2ANy5vm7AG8Azwbzsn/f0ep58z1NAoj/Hf1Mj+14Gqv05/rv29Dr/ZDz5HhX4AJjnz/Hn7cP76AW8Bjzhz/FP2lf3sbfw5HtuAo4Czvfn+Gt34bwHge7A5cAz9rDp/OSZSHtgihbQX4o57jWgzJ/jv6eJb327NPVK+T3gFOC2Jmjrv8AJgK8J2pJI/i18BxwHTAGeAi6wvkf5ADge+GlPL+TJ98QBdwM3evI9nRrsywRuAu705HsS9/Ra/3COBi4ERu3j+xiDeBYm7uP72Fu8jFgEnruL5z0KXAqMAO44eJF5E/Af4MXoAZ58T1fgeuBuT77H2RQ3u7M0l/o6rQna6NAEbUgk/za6NrItVih2bMJr2WI+Oxrsix2olCa85j+R5H19Axad9/UN7CNSdvO8ZICE6kb3xcV83qvPd3MJZbOZ2pVIJFuzN9637V1Dvu+Sfcm/6vmTjl4SiWRb7OwKYX9fKUv+3exVOdmShbJ80SWSrdnRe9GU7832xofY67TkcWRv0FLGqpZyH/8ottFp++z5luprieSfT3O9b7HjQ8NrKNv4LJHsbXb6+ffke3bnuf1X2JQlEsm+YW+tlHfnOIlkX7PVCljZsUj/V6yUmwI5+5ZItqax98Lcwf7dRaqvd46WMla1lPtoyajb+NwQqb6WSCQ7xY6EclOys+ODFAaSfcmuPP+xz7RUX0skkn8UsXHKDQcmuVKuR05K/jnEPtMt0jzTkl8m+aBLJFuzr7yvG44VUijX01LGqpZyHy2ZrZ7pbdiUpfpaIpHsNntDfd1wwN8dNeC/ngbevZK9Q3Oor/dZdMH+PsOVSCTbZnsr5Z11mNnfkH3RstlvHb2aAjnjlEi2pqWor6VQrqelqPLlmLljtlopb6PT9tnzLdXXEsk/H6m+bjnIvtj7NIf6ep8931IoSySSbSFXyruO7Iu9wB7Y7mP/PrZtHvUvXCk3BXLGKZHsOlJ9vfdpKSlH96cxsymEsh3Y1hLyXyeU5UpZItl7SPV1y2F/n6DsLbaXl31nz9vZlfK/Qn0tkUj++ciV8q4j+2LvsLv9vPVKOQY9S1MaOe5fsVJuCuTsWyLZdfaW+np3MiP9W5Hq671PkwllpfH9/zqhLNXXEsk/H6m+3nX29wnK3qLJ1NcNMno1tlKW6muJRLJLNNd7LNXXu46MU947NIVQ3kp9zb94pdwU7E8PmESyJzTXrF4K5Z1Dqq/3Ps1iU+ZfLJSl+loi2Xs0l1CQ6utdZ3+foOwtmsv7WqqvJRLJHiPV1/uWlpJmc39CrpR3iNfV1DcuZ98Syc4h1df7lpaiNdifxsym8742G93/LxDKu69OkEgke0ZzDcZSfb1zyAnK3qcphPK/Xn0tH0aJZN8g1df7FtkXe5/d7fP9SH3d9De+v8++JZKdZW84ekmhvG1aitZgfxozm0wo/5uTh0j1tUSyb9gbK2Wpvt42coKy99nd50+qryUSSbMj1df7FtkXex+pvt7LbYGcfUskO0tzqa+3l99aCqJ6WorWYH8aM5tOfS29ryUSSRMj1df7FjlB2fvs7vNna/h5G7mvbY1s2yu05JWyRCLZOfaF+lpWiapnnw3g+zFNtlJW95OVclMgH26JZOeQ3tf7lpbSF/vTmCmF8l5uSyKR7DxSfb1vaSlCeX+iybyv/82lG+WDKZHsG6T39b5FTlD2PtLRaxfb2t9fUolkbyLV1/uWltIX+9OEoAnV12Zj+/91Qnl/ejgkkn2NVF/vW1qKUN6fkOrrXWxLPpgSyd6juQYQuVLeOeQEZe8j1de72FZTtCsfbolk55Dq631LS+mL/WnMlN7Xu9jW/vRwSCT7Gqm+3re0FKG8PyHV17vYlnwwJZK9h/S+3rfICcreR6qvd7Etqb6WSPYeUn29b2kpfbE/jZn/WvV1Y1Uydpd/5WzRnes7A9CA94N52Wv28e1IJI0h1df7lr02gOtZWn+gRgvogea8zj8Aqb7elbYWGR27NmG7dXjyPQM8+Z4ezdF2Y7hzfQOBr4EngeXuXN957lxfU05kJJKdRs/StvW+iu1eV4dLbT+2Sqdkp9v05Hv6ePI9Ez35nl88+R5Po+02+OzJ96jAWds4bn/EEfO52QZwPUvLA+YAC/Qs7YLmus4/hN2dCNX9rUwTe+uQySnT66XyKweedWxj7btzfWnuXN/hu3Wnu0iTCZha09bVoUQAmGr0Oe34XJ87mJcd3NF5nnzPUKALMNaf46+M2aXEHHM28H9ACmB68j3PAx/6c/xzmur+t8EVMZ/jgE+BK925vuOCedn/zEpYXtcFwFPAw3hD/7evb2dn0bO0/wD3AbOBa7WAXr1v76j5cOf6FOBq4BhgPPBhMC+72voOQK2yRX12Ba/LGTbVpV7HB7RXisgLn1+305PvORC4CfjYv3zl74ADb6gi65ULVEcqn7UuMft13mCid1YmW+/jWqACaBdzjdhB6jTg6G3s++fjddmB3sACvKGdec+PiPncLH2hZ2lZwN3WVwV4V8/SpmsBfTGAJ9+j0LSaz5ZOZszn7fa51TenAScDl0a3mzVt+l3947otjg2kdX3BnesrStFIqjvOcDqB34C+7lzfScG87B/3/Pa3zZ4/QF6Xgtd1j0OJfN+g3bt2dKon33M5MBUYA3zQYHcKgC1xCcDn0e+IB/IOYLYn33P1nt38Djne+v8mhDAAMTCe5s71ZbhzfT53rm+xO9d3fTPfR9PgdR0SrlY+rtjg7GSaPGsNPk2OO9enuHN9jh0fuXPoWVoH4GNgAOKleqCp2m6hXAi8AfwHeBsxGcT6DkCpMzH2eBU4za4YCQB9lCAIoRodkMYBVzhM8wvTZL1pEnr3gXO/qd18WEWvVWa/l96McN8Yg2fejbTqsMlcCISA5cD90QtcUxS6CK8r1fp6ToP7Vdy5vjh3ru8fKZzduT6bNRECrysNmALMA77E64rb3rmefE87IHYF1Vwr5XOstn8EJgHxxCwa+lVXHxxzbCUtEa8rAa8rvolaOzTm8476/HLgG8RE1xndmBwpdhy4rFHd9ftGOOnc6Lba4kGHAH2trz9YGtRmoyleosuBvC0bNWDLmfRWePI9ccComE2ne/I9sSNNezBwtv0p+n0W8BFCgEd505Pved+T70nY0U168j0dPfmenjs6Loo719cK6GV9HR3Myx4Yc7/PAd8jZl49gdfcub69plbfXcLVyt3B8W1ZMaENhVNbpZkGJ+B1nY/X9Tpe12l4XXs8oLhzfXHABGClO9c3eCfPUdy5vkPcub7+dYPjllzNlirC2/Qsrale7n1GrBBz5/r6uXN9F7lzfQcB/7U2/4KoTX6mO9fnJuadUqyS5dnqVCY7b7kWMXEFIFPZBPXv9utYK95aRUlf5bC3UhTsZ9l+P92ZtCju4l8jOMPiwPbFcP9nEVxlJkAnIDnaZsdw+DjgNuvrCOt/A8CodaUAi8Eo7Pbgh8ftSZ/sbdy5vuuAauBRa9OLQFTAnQm8tYMmhrKlUGiuicmZ1v9jgDetzycCTH7wiGNGlpT/GnNsTTPdw47xuu7G6/oKr2t4g+3ZwAZAx+tKbuzUXaRvzOdt9rllank4ZlNh9IO2ugYVWNUaqhxsBrCZkVJb8gJUe/mB0eMiVZ3PbtDs3+5c36l7dPfbYc8eIDGI3wHwv8ghaz8IH2c1agL0cuf6tjdwDgMygHUIdZkDOATAnetzATjSpmFPXIFpEg6X9bqiVM/7tGzR/c8b1W1GUj8I5UTvYVt48j29gAAQ8OR7tjtZiCH6Ry8I5mVvtD4/D5QBPYDBqJVlzrY/Vtld0wFO2cl2mxQ9S7PrWdrxepZ2pJ6lNS5UvS4nXpcrtCzx5NpysTguWZFI6er4scAnwHXAt0A+Xpet0TYQAted6zvHnevzunN9Q7Zx2IWIQbs98KM719d7e/dvCeH/A/5E2MvGunN96TG/z4EQytG2VwFJQIse/D35nkRPvmeyJ98z1pPvqZudu3N9qe5cX74712cCEXeu7zd3ru9BhCbmQ+AvhDD8CzgVIZhpX77pJ8RzB4h8vSeo03nF8Qpd1A3u2Gt3VtajYqiefM8A4NrYfVPjkikyk0lTyuhjzCzNWgURBZ4afMGjVQ5WZ4Qg9/OIDvwde561nhhqrQzbmyZmpLptCCBc0v8oxbGxc1KvJ9oldnl7bN93hybxD8Cd60sAXncQtt1q/+J+vC4TuMTa/SwQAS7B69reu93w+W5yoaxnaekILRHAWGCi9dlz9FPDL3u8S8nPj7RNr1uYmKYa58n39PPke9o29b1sF9FPTyEmED68rl7W9kTgPcR766a+j/eErOiHmk3D7nTn+kLuXN+vjcicU4HOQCnQCnAbtSnfA7gtzfXiTAXTesQHbFj8oDP9N7HDNGlXZBJfTRfExO0RYAGgqEbkzib4DY2ypw9Q90iNom0KJEWmz+vVpqRMPBd2ItUIz7as7Zw7XDFMhvmN2cPnGPohusHbL4XH6FnaxcAglDDRzqnZdPTKyoIrZgI+M5IytXzZnV+WLXrQH9PW+Vu17nW1xeu6Bq/rzSMrKn9CDHQ24AtPvmdnVnDRVfWi6AZLOD8ivpkk9XhucVybifEJmV+iODYdu3UTzYslhL9EqCcnA8/jdcXhdV2I1/UmXtfymjvTa2pKbdVAcemqBKGKU8wIQPGyRIBahEosAlwMeBtex53rG+bO9d0E/J5GyWeP8e7DD5vv/9U394vsBsc5gNiHtQ0w3p3r67ydnzGcejtPBKF9+DRm/2EI+9Em4AuE4x009jdvWVwJHAlkAw/GbH+RLQelIxCrtIaToduCedkViAkLB69d0BOg0ibkezw1vOl8AQWT0lBczcb5ycz/oSPr5qfgNMJ0YJMCXADQu8Acd9qKmgUA45yZlJoJ7wFkFm1SAFantGJi50H3je550kMAPdbS+9xJkRtTIkZ59GZMoNBmGwTcCGDUtFXMsCsNIFLVMcfRajqqvRzVWeQE25uefM8llmNmS1ZpDwd4wP4ht9q/ip3QTgbuQUzCAcbgdR1B4xzQ4HtzqK+j6vElWkDfoAX0NQjzgppZvvHtNc4GZm9DjUstNT8AVnjyPdnsPR6J+ZwIfIzX5QAuM6Ht2KRERqckU6oqTz3+svvWvu8e9skBz924yxpGT76nNULQAmCaihNIRfw9z4s5LgN41/r6pT/HH/Ln+GuN6sz+AJ03in5b1VbBtFRPB5X8stCWuBxHrcn9n6i88kaEjz4fwxGr58wN5mV7B6xffIb3z3fxfXfPsAVZWqWepc3Ts7SX9SytySaie/TChKvUESt+acP62S7b+foE+6BpARPgHPukuOPV6QANvTnrL2yYQ+8dY3DjWOOE6783Rtz+jYGrgtbAB/83flR+YspvqM7NGOFEajYO7x49L7mmgi4lazEjSY9WFFwSncH26fd+/7KJT7W/Aq/rVbyup4AVwBsVinL1rPi4WG/wNOBFvK5WeF2n4HVdgtf1Il7X43hdWsxx0c9LGtz688CVce2/eUu1l9XZFlTH5sO2oXptTs5AODBYmDf/UZw858n0tI9ebuW6etFfae6lY9s5lvrasWxcGyo3iQE9pVPVQwDla+JZPzflMryhkxAaB4AH8LoGRFu0hPEk4GUVY8gHm0cZB/vmc6hvrjJqxVvf9Lj7u/7WcVci1GYaYgzvAyxEOPGNd+f6Yp2GYom+RG8iVIEAx7lzfRkANTZORjT4oxbQa6j3PThLz9J2aLbYF1immRtjNl3qzv22rzvX91/gMmvbrwjtSnRFWmRLXjAzvsPn9E7+hSVxF0UdWT4HVgxbLXwaf+oiFBRxNbWUrYljxm89WPVDa+cGfypqyGSzP4UVv7bmYNNvA84cMcfgsY8iJ5w/RtGSK0wCCWZtF3XDuCpFodVmJRlgU3yrVYD9s97HPF1pc04F1JF/mJf9UrB6zfDyCgC8bVtzQpeO7bDs+bXFB6MoNUXiFk3syYvrfqxqL70QyEeYnDKaplebhSPbUsyFtgkAlJiJNcBLeENH4Q0ZCCGzHEgAfsPruqqRNhoK5ct2xUy2k9xg/f9DzLYpAL1XGzbbllWO+M8fNco7r0YGnjcx4oirMf9q4ntpHK+rGzAICAMDgWLgIOAqIO/j1BTuzWjDE23Sye6UmfyZK+UF1V56vi1+re7O9bUHcOf6erlzfdnuXN/d2/N0Ng3nYbHfFXv5CmCu9fVWd67P5sn32BGLjdYIH4m7AAbe+umR9qqETpgmvVeJfgtmQI2DeIBPjw7/oGCSM85pHriyFoC4SISzF08EYNQfb946dJ0urivs+n0RPkdP7n7nbckeCeVNC5JzqovrTX2tisqUSI2QS285X+B824Tzoc7ZpA5PvueQ4XPNYwYsNzHFSm0LOlRs6nzEpt8BqFl/EphOgDOenPLGSZ99/1Dpm788y4X6ONLWZ441St0AKIqZNDY58R3EA3w3kLDQ6Vh3aOeOGDUKHWvD5riC1dFLHFGiKquA/yEGj1sQTi1T8boyAQ4oWnnqE1Pe5ITgtLqTAIJ52UaKlrvUmTYt1jMb1VmUTox6sbmxwmOetr4+DnwOiuoal9q79aREChenEFkWlVmmWV0U1aCaa0oLEkY5ksJ/AWxakOIGwBv6GLESBUtd7M71jQBethFhkLKId3iGuKlVqlGrYoZVes0qsL8644lZJ9x76WOo1S9F7601oTeC8RdcEojLKX/T8XzlCepfWXYiU925vn6xv8GT71HsyfOPc6RNQY0r/C2Yl/03MBOx2sj25HtuKGgrPE7fO0491JPvaYUQYoUIb/hY5xase04fcvPH5z590hVn6Fnadp10mpGLgF6mqZRa3zupcRvmUa9Knh7Myz46mJftQ0ysRsVlfH9vYucPBjla/Y3a3oddMT7H6zo+mJdd+8M3d2p9NgdrAdYlpn8cvUjBpNYkF1ZigFkUl1yysFVnInbFqNwYxylFX6m2iNnzgomGCWALK8rBiwxCceWOt1ypa2bGx9F1vWinz6ZVbwLTgdaPHHKZNYE1r4qscfTcaoAwwQgnEQ4N+EtNWDkdQLGXVNniCxse+TcwxZ/jX7unnbldvK4ueF1X4HUdi9e1q5O0gSfYpmNXDGYZPelf/Y7TXfXJn/Vth8rZ0qnt4kbaaKi+vhgY68n3bNMMBODO9Tndub7/uHN9J2zvuL/7a62MelPNK9Htf2T2LgPQCkwe27iJW9fXvnbj5uK1qmFy5p8GAGf9ado/fD588vbab0IutP6fhjc0u0pR3p+cEM/3SYmvhVQl+dU0VyR6oFmpcsYfBl3XmdiTFzpsiUtedef6XkdoJcciVODj3bm+No1dKFx2wBaqY2erGZHkAx6a5Wz9SxVqxYFxGb63gBmIycFmYLg/x79Rz9Kcr/36/HfPfTGPHmugdSlU2yHQWaHKIfwn4mpNrhxncLy/UgH4pb8QXb1CBe0sh9NrAPKGXMhTgy8wlqV2iGr1Lm6q8WaPhHLZuriBAJMPPJBNcSklClCxoc58htf+wYknvKM9DoQ8+Z4HwDK8m+aLJ88QD44C9wJHLM6k/Lc+CjWWP/Bx80owDRu1Jf0Bll+oj/tu4IYlj6mWF/ZFC3/iw/GP8+IH63HUihnP7Pg4lhrtJwOz/46Lq7ioTbt078cGb78cYWSwclpmOEJGOFwNMM8ZlwQEEbbM9xCzqVTgDHeuL+O+vz7sM2jDYm6e/UXsigdPvqcrYvViQ6wg3wNQnRuJ7/ix15PvWdPvvcH3u3N9zW1XG4BQsZcDT1XbuWlpJzOimjBsvknOBNG/S9vzMSidrHuuBuVRLaCbteX2r6x2+sS0+SaAYSoXH5T78c/AL0lU8p3zgZqv4rx4liwlUmNDdRjr/xpiFNbawF1Yoj7z3bQHTq4clWiLX75mSJdHcqbarztiUyApd+MfyYP6z1qc8HThm0zgdveVNt/MVx+45A28rv4ApuF8NqHzh93i2/+PRPfr11gD2TfiVoz7u6w3X+1hDelTs5TuwKVaQDcR4QlQ73BEv/cPzDro5RFj0xN/WtWpeunoU5f/8bWBMr+J+3xnOQWgZuMxjnCpsODYU/0gBpwHEEIbgGBe9qpgXvZ9ztaT6yZ5qxwONqoqCHsYwMGK8LkovGr+2KhDUh0/uA/98YKTvIFbh9/C3217jxcNOxmy2MRVUa9OPWil+P+VNNeTD7RpbXZdL96buEh4hnXPS+e07dVhYefOpaAoa2e6WG22qRtMb9lczC3LulG+9A7MSMqXiiIcveIyxv0GYBrOxeXLb6Si4BLO/e6wVldPOrAw6h/SLHhd1yNsfO8AP0VMZQNe11N4XY2Oa558TwdPvucFT74n+swPPEQVq54lRmZ0Rfm+O9dX/054QzMQ2h6AI/C62gHoWdqQXw/Rnu8XNNphmrUIzVyU3ggH2Nhr9/Hke261tCjYI+FXEE5b37tzfd229RNnd1fOVkFd1wrOuddeN6n99IiEswD6roQ+3yZy+vTSbw//Ni54/VgDh/UXs2dW0yN7/exttd0kiOibB4HHaitVjAhfevI9yimdOhx8Q/sM7slow7mZHShXVRugX/QzPZ79vwgXTDLIey/CiTMMnEnzRiL8WgCWWf8nAjfqWVobPUt7WM/ShoKYzCi2ikMBTNNWYR3bXbHV5MRljI9P6f0ozta/XQ5EHbVe8ef4ZwPUqPZ+adVlrToXVTEqX3TSrB4KtXYFw3pLHvrU4PhZ4r0YN0hZ9v6xKhEFVJMu5XHcihj3p03qNPDjiZ0HqTeNuO28UkdCOUIDe1G3Bz+My3rlgo+1V86/E69rixX9zrLbQnnliG4ZNSF7EsAPHQ4pS62p+AKgfK2YLFSaTjY6DGWd3X4vQpA+5sn39AOO77fCHNplA5gibONdLaBPuT/HnvvK6TZuu8pGWIV+K0wOnp8IZhzAhIsW/jQMGIJw94+qKuhUUsLZfhYArLPbOSHu0mHuqo82XtamU9Gt35iOrNXgiECPvzqUAQyuqo4D+CMxfjWQhTd0GN7Q5RWK8mIYMEzl8LYVRc+1qyyyOsjMaOBA9TLQRjXMmcBJiJUdztaTcaT6L0Q4ON0BbHbn+l7c3f7dCaIz4J+1gF526+3Kkfde7LA9fImC4TRmANTY4Y2TbZu0gF6oBfRzgCQtoL9hnRcAKHEkDnXn+m505/ouHFL1+pywqeqqYiYfbZt1DBB5zvHf9X3VFU4zQrhokXCa/MFj+/zZ45yZuZfZWNvaxBGBm34o4z8b/tuhprQif96Edp71s12UFiRQvCSJwqlpVHxr5+Lff3ScMW3yNetmp8y68bWeRylqze3RH6Oo4SOBbxM6/99XwOp0dX6P3M/Fi7OwIxQnK1j9DWIyBHC7nqX17vPGSd17rlL9r//f2uz/GzMu4cRNowFwZITrHMb2Fp58T3fTFH+bcHmv+NrQIACcaVMqU7Tcs4N52U8E87IXNTinL3CQappG67D4zXPi4wBO3DCyk0J9CNivCLt7HUtTM/ko6/h1iFUB09tq7wC0L1AZOUVMzJypQhnVodAKblCUw0OmqmRurmtmTjAvez1wwjHq30UnD5meAlBT4mBVZdu6Fd/x5RWcpszBYTgBfFje14piiJWcUvOtUdVp+rmFqxm5YFKPY/+c8Z/8Px+P9ZJtMiIPt7oaeA1IWmx0pNBMx6aYSQgt2THbOO1l4Nbsv4y/Z/Tvu7RX0cp2Q9SFAJxum5KLUHfGI5wN6ycT3lABInRTQXj+JwPftS/mtoc+NTh6jrkQaJgw6SVrvItO5CcBLzhrzSf1LG386B8evnLQuoUgxuARbIO2Jeb1AEs6KACve/I9rXo++cTha7vrGXPdYliq2eBkw9yUD21rHYcMmy8Eiv8Ak55HbsKZHGkebZEQxpdEapUFJSvjH13xS2uWfNuehZ93eObNl8P/26Da6gTSakdd5OWorsu7XptqeSrYTLj8J4PciX+CWg2QF8zL7oEV6qWYxtGIiYsXmKpnaXOOW/nntbb41ZZ6NhIdBwC+N022yFthhJN/rFx10cvR7ytS2m3lHDo1S/Rh/Fb6WpblH6uWVMUpLO4oNiRV18WJ/wJcD3xmKKoy+oBjkgDK7PEP2JP0Xxyp/gvsqfOeKVWUKXhdu+ylvctC+bkbhl/x2lWH/Dy/TdJUULAlR5gT3+Mdhxn5CqBkZQKmAV9HDhub27YNEQX1Ol+Ex/PDDF0UuQg481BhekaBD7WAXmw1/RqQu6GVwv+Gio66+Ydibpk1hgenvZ9GvV3la4TtsQvWyi5HL026vkg0E9f+G5ytJx974pzqjoNiYtDWVbY5dqnRgQFVIufEpykpa/GGqi0v2VcO7dop97CunfgyKf3UIesCdSsZix4AVjaxU+8ZE+HTpyL9x4wKf92z0NxKPaeoNWmKvdiJUJ00KU/ffMyg16465GcTHgN4/1j1UE++551SVf0Y4MCUipKMXuUHv3+s+uT9l9hY0U45Kmo+0AJ67ICuAzjNih7JPZ54JanHMx+FWi1c/9/IaRrAMerMsitsvqNPtM1Iq1IUvl/c9p1IjUpYZdN7x6nnAxS0VR4aohXVrfByJhg89V6E+CIbtXaz3BTC5FkwdUyFinVxlBYksDmQohYWx08EqC3xULUu+1uEPTrbnrxobmL35yb/Z/Eko00JrE+xVU30qEdZlzjKcvL4EOGpnQLcklip/HTXN1X25CqIC8NR88TfvaS1bbsqxN3Bk++xv33Fob77bjn2rz6j7v64kXjsxxQFZ7iiK0Zl53VGbVp702SVYqtOIGaF3IAzAY4pqjKGl4sQ0z8S4sNAz+qQfRRwLFCFsHHWPdTrB6Utu/Ho2ymOTzkLsZL+a2z3wycG0rqgmuC21NNPDxDJn9oVVZBR0PUk4L5TgtVLVRNUu1GJiH4gGH/B8tccLxc74wxqUsRA2isUrLvJzrXhdRlKMXmOtz4M5mXPj70XAEVh3O9xN196/eJvDEwFWwez8qgRC1btSv/uDK9ef9iHszckvbl5WQJfLjmcUyof57DqV/gyciQAC41OtzY8xzJ9nN1lvUnOBCM+qcbofs+sfDKMIoCwU4lMQzgcBoFuwNsNfESiNsNbVYfxFdAhuuPMP4106u26KxEJXxIQWkAQjn5tFMPkpv8ZtwPHJYWr1YemvUfXkjVgOZw1RM/S+vYqZKABfH+QCmLCf70at/ZtRYnw/unOmvj0aPSTsoXPxuSOPehX/W7hDTU3z9pmR+4mL1w/7FHfunbL1qxMzF86NiNr9R/pVKyPyn7FllZOds9CaFsVI+pMk4+eDlccuHHpXQCtPaVmeh9h3Tl4cYRB4U/WAS9YR88FGLp2QT+2nLD0P3jDrMsVWzWmaQsrCmFbxOTw+QbvPR+eOvrp8MNmlevC2tCBZsWKq6hceO+J4dJ+f7hzfal4XY4kZ1U2wPxOdvPLwxR8QxSm9RZ/4vSyLX7iucBxYZsyAGBa763E5B/BvOySYF72ecARP3c5aGa1aic5XOW+/e8fDkusEs/Cz4mJBlsm4Nkpdlkou8pK7z/6t9Axbn+kG8DCzPiIiXofMF61G9WRahvl6+IY3XXh97Pj4+ixPsKIuSYHFMLl4827HWHztD4r6h7gn6Mf/Dl+05/jf6pUz3sl330pf3e318ZFTE5c8ReHrZk3kvrECbO0gF6lBfQCLMeHys3OrpeGSok3jHmqvYLk9B/4z+9ilVCQnPEZQJ9NQa6tuoVNJQeDiVGjKoOttIJfAjcaihJXqap8aiSkXD0/6uBbx9kADtM8x1VmKoOXmigie84J93weqZsJmaaCabIUwJa4HIRXdJMSV1t96dG/hY6JjhZTeysZwBU1iuJMNgwuDZU80PbLVeb3B6kvrGinVCHUOMMA3Lm+Q6MOVOed9HB8rQrxYWhbWYLq3ERcxveMN4Rv3vG2v5MfdHw8qVJRHBdktC9PCjquBRg/SIk3VCUdIRSfS+lU9Z5y7saC2HvcnAzX3mBPOvde+w3n3Gs/8px7HaE7rrAFPj7KVjdJ6R80MU2Fmo0jqN185K0Ip5AJgNqjaP35JwaCKsAbQw9yfhM/yo4IGYoD7tICehnWgFejqueOnL2ye3oZGA2EhL916+ZwvLMf+kfxyRePW33QsRt8F9zlejRgPOx6PfJwqyc/e8Y91DSF41r12tMA9aFl3mvXKQrR2fo7nnyPz5Pv6QBgqeauOmWaccbI3w2uetNhP+tjR+3dn0fo8WOCurkwnooNzqg24TEre1Pdb1zYoety62M0qce3QPjXrvVmTj290+Zx6UPnb4xPxYbJqx8tLffn+EddNa12PEBC65oE7bzCtXhd64BQvFLbzTCVshmpWWUAB6yqm9csUxQRpz/S9vvplu9FOLpzUFVVwdzlK99sX7N5funqeBXA3MAwvKGVTdHpsWTNK7oo8ZdU1v2VRp8Zy3lyylt/g3LAlEjf3wAylOKTB+SO/sOd63vPCnuid3XNxQDnTjaqou10LAmxaWEywCy8oYpgXvY6xMTJQIw3Q2MuOxaYVFNqizNq1eMA49VT1BW1NmhXTGanjXXHfY81YQZOskLILgQ4dZoZHLqw/hGNM8Icu3IGwFFWrL7izvUN8z14/KvGw66NyVllP4BQsS7uqDwNYJrqmY5kXQO4tGKDM/2AOgd5qhx8saAzfH64wo+ZR4XKScj0GYc0eahm+3VFd3b/1eYu/qMVkWobYK4GnkFMHgHou8KkoOikugnrheOTws6I8FkJKypr26d/165/KRU9qmsArpsyv90A1z1Rn5xFAMNXzUqzvr+MpS3qXr5WONaFE9Zimuat3xrc8p1BUjWP2gy++fyFTUeO/KX12Dsn/cmXvge4deaY3seumb52RW3Gip5K4ZEAf/axVX12lI3842wVEZvSMKb7PS2gj9HOK6xz4Au5t1pGT41+eLHNVT0v7XpX/LTuB1QCDPdXcZyl/s5zdYn0Tj3/o13s3l0XyrV22/pVGbCsPSzoDB8MVQqDedmVWkCvVWzmGIApi9LXLHc6Xge4ZkZ9NsS0UpSn342077iZqIPXlEYukVVd2ZeH+z1x3Ue9j/tuSgfPdFMIt1KguN3g4nWWY4cC/AJmpLbMjrnesbhKVY8CPui33AwnV8HGhCSuPebOczfGu3AaYbqsWjfhueoriVR1jg4UcxEB+BXAacOLayK3fmUQHzZY2h4+GqGGAUKJ5G70pl09sKr6ngsmGlvcrKuCS7L/EtuMqkxal2esAmib8ZWZ2P25LdSUTYGhqBtWZUBhhsmMgyJk2tVgr5oaFNPkxLKKKR0f2PwKgD/Hv5F6T+X8A5699a6knnl/JLpfXXnAczf9XJJcPWeVFcV40a/GY6apVCq2apZ0nvR02FTHRq/3bHqr8h4LbEntioWw/WyYmoTwqj7Sn+OvwBsyeyg1o98/RmV1OhR0Nsxxw8zJpYlKOWI1MRQ4pCBDyfr2MCX97RPEI3fIUhtVqy/EqM5cEMzLDvpz/HP9Of5jE6rN+277JkJ8LczrojC9zWAVbA9SH6p1lyff0//iO2xHVDgVw2kY6WdMEwoAFUYiBk8A5rd1BJu6//05/iqbNa4OXGrye+ui7tWqcp1NMe+1wZ+Kghqp6oBR3fEd4G3LvvkCMM5Ra3LepMjJp0wzftSztOcQDmtvXfKLMfjc38Qz5AjZHEOWmAxcirpucjqRapsDxaxGJAGhTd/SoKtbRU1bTwkl8UmfNbi934J52aHfB4QmrkmDWhVD27zqTODAlJrKaMa9gwDKCuPtAHFpYRAe0hlYyUJUxXwutbLsGYCh8xOqTZMfECrh1xHx06mIhEFv2kxzybW/1Zjez8KdjVqle8V6JxgKis1Y0tu/cEbT9r5gc5pau7gDLLfWIH02rxj8wzd39k9XSo9fZbapSFPKuNv+2aGIle9LeF23aTU1LztqTQYuNe0AEw4U87WpG1NZ5rDX+R4E87KnQHiMYi8GsWISiHSbn4SCwgRQ6eD3yR6168KOopzByCnG2wgt3t2IQbsIYWf8HIi3Rcy/L5wo9P6bUuD9o8Vk56B1AROh9esDfAZM6qMEbwiX2VqXLkrqDDCnO5uBVwEUxRiixq/DbpqcWF5OSufKKWD+BcyNr+VS70X25z4fZnsyXK7lIsLymnxhUJFgKyu0xOWGhBTuPuL6s7WAfvdJZzy7/osDDvkdYOAyg3BZb46YOOTn88elcvrMEjvAj10PNh869Mr89umh84F5/fsVOcuSTDptgmP18KMAwbzskq4lazccUVgX9Zrf4eCiIED74vIER9jkmOqN6W99V1E1dKGJiWmimDOtY6+9KDD+1OGrZxMfqeWElX9xx7TPEmrH0SG6mj8trngjYkE3BGEG/H5lG25COP3ebOVqePO1tes5sqLSfLRqA+UZ4TDA+IHKKi2gbwS47r89c0a1Tnv/vbTUPtOz5yZs6CmEd7/CCPERGxVx5Q7FXrrVCm9H7HKaxeve/esQEBmygFVgdPTke1L8Of7SSLXtSQMu6LJG6XDmHwaJtUaw2zy72wRKMsOkFtrpaK2VFLHiqau65M71DUd4QycDRFSb/sS3L0djzNCzNOWAkWu62xzmPITdp70W0J9YMaz7xor1ce1WTGjTa8yE8KXAryacALSb3SZrvaGoGX+11zg5OJX/LP7VOaHLQVQV/mdTUo/nXYiXBuDZUj3Pd9ryPLNtyUZKkok8dr7NFlGxnzUFXBW47jY6vGkWKIzwG5iYdB62mYr1cWwOJJMzwWBJV9hYM4Anza+Puj3RTpmjVnFF1n3JDjKb7Sq3vT75MeCx3x48PHCxbV7vi9fhBgjDh/b6sCbcub4MZ+vjn43LGH800DOu7QThqe0ojrMlrDrGFreW+V0Uuq0zOUw3M148w8wFXrInBs/9xJU04pKS0rT1Npv7q8Sktq/9IQTGF0eoVMYpAGf5c/xRz2Ic8HDwwNoh9w5xDDqlrHLYozcF536d70lCrNLbIGxxlUDFCY6S8yD5hi5rarEX9SJsZ1rs73v/+UipAhQlwbNnqVXhFR0dwPBS/YkHk7Me+E1RzCOBOdVOYeY49zchIQ2FGtXEl9q1YuHaTfGnlNhVZrvX/tGUfR/DEcDvWoHJ804nh3XpSt+qMHGKCD2IlPcKA/cF4y84BPjJv3zlAgO+GT83I6HrAvswMPsD/Rs2mti9wqxeFf/E7A62DhtcXN5vhamEbWAMrIg7rm3oGbyuG9p66IRIFVhzJ5/nvxo5szfCh6EU4UFNbYeZ1XdfbiO1gvt+uWXe5CCgf3Pn74gB6GAAM6L0AShfE3cLA/iV+r9REd7Qxn6jtbbAI51LQnGf53GeFtBFlQuv60ZgGnCxf/nKv2sr1F+W/N6+ZxXxrPot/R0jrLQHTjEj6vhm6XngnG/nOz35nuHAr1f+GIk65lz3wBOvf7nioV4XlKvKN2c4fuWl8Fl41OXnAqmLnA4GLTWxG9jL7fElXxxRm3rMnAjd18DXampSNPuQJ9+jJPVM7aU6Sqhac+a17lxeDOZlrwAwIowLBYWD93SPuRwYtrItq/utpNPhuplw5df+uiIdnnzPt4hJwZEAI6cY3yrwqAmbb7wqKTneqHJe8gtm19J1SrvyTaxLaj0PIENZG+mirrMVzk5DMRTK42B1D2OFP2dBwYD3+/0dUZTBAJ7qalyGCTbu0AKBunfIv2WegGbhio9mZ/R73+PrUGSevLn8IDaV9XjUneubAIz6vl8H5YwlkLUKbpjx84/ZwaknRs8rdSTw2oFnjVzy9Olfw23gdZ3uSDDeK8uqHpb8dzxd1nI4XldcaEXCdy/rGa1spkFShyqzy1Gb7zJNRm7wpxKutNF/uYknvSoxY1XcOTVARv9SpU2fso366Mxs4GaEuXFBUvuqXvZ4QwsFE6ktE6LOkRhmWGJRZ//yot54Q5UIM96E+pET8LpOALoMq6wqGlZZdQCwuuyIYueD9gz0bnSc+HbvfkuczsHx8XHvVAmHTKYlxlM12OD+JQa9gkpR5eaBXxyyZmHOH+1TGk6cd8huO3r5c/yrER6HKtaLrgX0wOweSiHA+ZMMTv8DtxlRUR3GX0WHdo6kWKqWiMJPCBtyLB9Qn9YvjMg9W4cW0E2bw3wMIZABHsfrOrr94FBqzGHPAe8pQo9flRiuuggoH9f14DBAl9L1Q1OryzFqMrRIddujge8QM8kXgAPjltbaAXodUGwcFql8qdqpMKWvSFl38XiTy36yVjNp4c9TMqvJOLCExLZCE/D61BATw//lsMha3ly7nvbhMNcUh7bIitSUPB6+6PkCQyx1TZPQqdWjxrurPrnBStX4f8Dqmk1HTw6X9TrNqGm9Vdkge0qAeV3rtLvHILzIg0DXZ1qnLfN06/LdMZ0zV/ddiTOtHMIqxRP7K/OAU/05/gVbNOYNVX529aKjp182v9XDNwXnAvhz/OX+HP8f/hz/d/4c/7f+HP94f47/9xOSS750JIVRTPBsXAbCuQYQYV6KeKlYkaG8VpGgHI/p/FDstX1avfb0utR3pqnwec8RLGvVeg2AapKvBfSajocWZ+vnlHPb1TbWxdmay9FrBlDlqoDO69kYVg26zYYR/3OQUmnQr9yhB+MvKEM8j0nAQYrJEwesNodFG4goRIDz3jtWvTKswoY0k86Di2cfMHfhg+eMW3DlG9m242++zjbz1mvsPDgkhUpFuRIR8zm4VFF4Nr3VBk+3LvemaLn3ItSGRwfzsqOq2f7VToUNrZRYR5jp1v+H61maByt7XnXIMQ5vyI83NBdvaDHe0EYALaBvoD4lYaw38nSEqhLgxcpNzrr88xXr4/pXbXZGbWj1oUXNgD/HPxG44ochdUPYEXqWlnBKj/jfT+ycWXtOx/Y8nPgiDzvfSY0AFSV2rvlBvL+/de6fXFB8BQVtRC3d6tXOWCetvqqjZDCAs82EeJTqaJQCCz/PdNSW21FUk2mHGAcArGqrRFWZJzZI/fp+zOeis/4wo/Ha31XXupXyBIU1afZVAIesFa+TPXV2pLr3i+a17dpSukEI/5dOV9HUKhvA7ZtKN0QbVGpTdEDFG9piUru3UBQ+XZuuUNVhBmAch9CcKEUdlq0Ze7AYV2IFMkBhUpufhEC28IaW4Q0dVdLGeA7AvVpJKJzu+qjwz7TjncW1DoDWWWUKcJ6i4HB0ilQAjJhr0j6oTqspcaDaDdJ6lQMcr51XGNbOKzxdO6/weu28wmldhm/WMg8ppvtJ6zc6ksIzwFyVMaCkSBG39/h28ppHJ1efWu/DD25nDXSspUZVlSVOpx94v0pV7YOqqri0uOSrBMNA76xQayOcWEPa5+9MveoOX5Hzy3cn7XIe8j3NthNdiRwO4Mn3JLx/rNp29ZZDYaVRq54zm15rOw0K0f70ULBdn9ITtIBeZ49y5/p6EZOhBfgumJe9pSDxutoi1JMgnCkAHoxzhRN6nLKuDMxnEJ7QfyIcYvpd8esXPwHdVqS07wTMVMB566wxFTfM+TLx+beNp8eMClePGRVmzKiwOXhd4Jy2ZSFMBVzdKh0vrN94y/RgAdd3XetUHQbti6FtCUQUqsJFjusATVGY4upWUQxQtlq8j6YJnqraUl9BIZeWlDZbmruFZpcfT615nOdrz+bS2rs362bXDxFxjB8iElTYgfaVBVfcXL70ztTypbfXVm8ccSwxyUb0Lgqm8ObtOWZUOA24rOs6k9P/NEisMp9CUQ4+Yr5ZA2A3+HTW5fM8/hz/2K3vZpeYm9ROTGQuXDJ+IvWx0SDCSHoBpQOWm7n+5SuDE5x3VFqlCDvVhgamxpd1Ri3tQeWqi2eVF510TpnN1RmxGonmZb6kI2EiNgUUpVnixq0KVZMBnno/8kKfFcYbF0wyGLzU5OVvqvik9oOeiPSx0aT5z5UEE6gOOTBspnndDTZuvtZWoAX0z344SG19x5U2fhxZg2qrT23pz/H/bCjKQXbTXF2uqvyUlAgipnnwo23SyXeldkSo9K8P5mVPCOZlzwDw5HvaUO+EFBsSNgURPtcJ+AkR2vGVFtAXbuenRifG/UBoq/Qs7TJ9dIcgIhlDYcV6Z+zxByPU47Fha83J9NWtoSgJE+FvcBhwVbHN5ljhcHBP1xqeaOdk6apkHv7QJLkK4lvX0K3/OtUs7/l5anq1AeDaqMYK5brVruooIaHTR4NiMtIdAeBMr2VeimMAwO99lU+BAoRG6AE9S3tUz9ImfzYqfBhWYhjFMJ9WTc4A8Lfu/mekwu0A+Ku3EQI4etXUYkf65NKEjqNthoJ9dVk8Sg1UOWBuN4UhFfTvlfvN/51RUn3UoCox75qREHfNTlawai5GA6WqvQI1fk05og+89pT5G0YfpRLoxBjqfQ7+BM7sXVwwsrGG1mSajwU6iSiZ0NKkujzTlZlhPk0/ZjYi491jD3f+zxyAgxeZdP4lcTOAEVY/tTnMaLW7cYjMf+OBJ6xt3rgXNrXt+ffig7RAoHNql6r3re23A6V4XYXREM0YoolLooUX7lRgdt6GjXSuFSrqjHCYWzYX8+q6DWPvuGXFyEpVTa5xKD0dEb6MaWcTMQUwdpY9rRI0CZHu8FRExx29Nl2Jv+0aewHQdcyo8HHAGi2gr7g098NZN9m/7piWUO7Gw21YKeysXKXvWO2NB3KBxVtcRcQG/h/iB85ADEZjsbwWncmRv7RA4G4aIZiXvQFAH3vfI8C3h66dHy16UeceH3ImvXLd3G/OA1if2GpJH0ehARwQb5oQb9Lx0KKZBZPTy0AJ2UzytIC+CdHhR6zJ0tLAXF8dctg3+FPYvDCJOFf4Z/dxG6cgHJeahWBe9kp3rm/uy5Gz+iO8RauB3xH2Ph2xQhsJXAsKRk3GZ4vueHmCR6iVAaiMU4pMWKiIVdO1Y0aFPwqrVNgNEjtsVngj27buUN2sQUyYvmuSG/eGNiXktF9bvCypvRZa0T2Yl20A6FlaZ6hziHpSC+hleF2f9lDXnDIz/lrGhI+irVJcMWLDnMQKM25tovLrIeSFakQWS0SmGeF81C+rusZ0RSJPhGy2ZtNUIGyFx9sNbnj4EyMaW0lS0ElNJ0dCXKcICK/mS/TRmXPAvBxIa+UpVTalpgO4LQGqrWmt0KWoFkQGrDr8OX7Dk+95A3jsufRWHF5ROcRlGMrkxC1yZJwOvBTzPZpFb3msiUEL6JV6lvYhIoFJdDX7BNtnHqJSWj8rWc0LwM2goI/O7KnYjHwzoiwGGsYiv60F9BUNG2sGFqIotX634rBCgY4lJgf9ZeMjDF3oIFLmIAnYnKwy9MjNdI6bZp7CtMDyNolqeFEcmRto68n3xPtz/FVYRR4QmrtL7MmLcbSadgFkP4XlMLk+06BSVRNMw7mxylnjQ6hLnyamqhZw2Nk/tR02+tD0kz56c8FERDGb8qeHXFgYLqskLmMcEwYo/c6cCt1D61qltfJRaYWUawVC1i7sqGCoCgNrSxhp++2yVKWS59YYHBN/5o3+3Kf3xqRnm/hz/GFPvmcqcFyi+5XbygJ576RoufHAg4ai8NDF9jvHjArfi8h1/auVX6BRrr1nZehCQyu85TMyU6qgJNnk8f/YWZlhI7Tw4uQrnxj9sDvX18aWueCOeV0U+q00cYbrwiN/RoRNxSPSysbmh/ic+kIjUe5BTDKPQ0QsdADm4HV9hkh/m0R93QOx6PSGlgADO3ld7b5ZtWbeKoe9TZfaMHZRIOlmqz/KgaX6KM2LiLGeBryqBfTQrvXsnq+Uv0E4bA3x5Htepb5c4w/+HL+pBfTxWkD3A6wjfcFL4bpJ6O14XX0tJ5iHEQ97OeAN5mXPCuZlCwd1r+sivK7ZiMHtZMTM6y6EyjM29/UOU8lpAf074Mpq1TF/iasjv2X2J6KoZQCumvKLO5ZvdAAYpu1FhLAPIIREZnJm9RAtEDhKC+inaQH9jwbtFoHyfwAb56dghFUqNznP1EdnFuujM2PvsTm4D9H/i4ABwbzsY4N52QcH87JzEF6ksff6GtQ9PHcgTA/D1foB/V5gvt0gEWDEXLPglf+G+zkjdRqMJkvXl9C6diyAUat20bO06EwyDxFG8jvwNF5XD2IG2HPskxhhm5MIkKhUtweq8bpuqWtU5Nj9EqBDJDLj98sXPOjP8X/TVPfcCB8DS4FMpb6ebhBgw7zkjyLVyhUrJ6afpY/OnAo8DkqaMyVcnnlAGa0ikagvxZmIQYtOtWGwYt4b8BymqW+22fgxObHVEqfjmAp1i9d2hCff0ynme1Qoz2VrYgeoAA0mAY0QbeNEhEbj5tjrmhH1B1BciHf3HITWpYytB8JmwZ/jrwHmzu4uhFm1nUttEbMfUHNuoOrpk/4260Jd5roVnjjH/l5RXMoku2IodsV4sEsroVnssRa1R6F5llVtLlpo5dFIRZfFAM42P+fefHvf87ByiU/vJvq/tmhom1I9rzuivOYSgFBi0nIABWznr1w1pc2Kkx+Yk97nYavNHzYluDKN6vYYNWml69MUVrUGuwFHzDdRTPO1aX+vqbxyvFCzBzorOAzMjEiE++0ikdsKo0tRqPio15upS3cVHUBROMCqL98foYHZAKzSAvoyLaD/sj2BHGVupv3zW6+x8c2Jxpp7L7YTbC8mJGD0dOf6DgAetcWtT1yxdYDRz3hDZXhDFyJS/B6GSK15InDhVtoEb6iWxjOznYt4H6NpfxfjDa1vcO46JwzrXhseZYeD8IYuxhsqij1EC+gBS048sTsCGfZQKPtz/OuoH9RvAKLxpJMaOXzmmMhwIqYC0BGY5zfcjwN3gclJ6rQ7gvEX1Lma43XlIlSxUTviTOAwvKGJeEMRhCpvvPXvBXYCLaC/O2DB3H43jbjt2ycPvoTTTx2VPLHjAELORPS0rsUTOg06++iZv7+GN7QGb0jDG7rF+ryjhyqvkW3vAF81sr3JsNI0dgT6BvOyAw32VSEmFycBQ4N52XV968/xP+/P8bv9Of65CI/PUdSnO10IoEDndsV1qu7FWkBvsphrZ3L4FUUVXWqLj3TRszQNMeCZwC1aQDfYMr3hWkR1qJfZstznwzE1fi/CspPCFiqkZkEL6JUIh8LPEWEw5yBsyFQXO9ss+rrDxeVr4/8AShBesKRnleUrKpxdWhZdwd6HaR4OkBkOGzQiSP05/koU5R2Ab5OTebBN6+iu8VCXLGFQzClRobzVhNByrByC0DpdsxODZdRZqzdiwApTbzp6GxiMyHF8ghbQP0eYHvprAX01e4+xf/VWqHBSGxem/YOfRkipMN8f+XW9EvDBi2w8fp5KQTvj67ZKKBtLrexMiVDU2qhxROAov3Ebom8ciOdtWU3RIc+YporqKG3VfxlvAnGlKYkrvukl5pHhst4AR1m5Fg64fdiN6WXZyZndT1oPiklkvY2L142jtRE6HkB1GH+84nj5opttX2GW9JsJ8NNAMQRfNc7g09dq2q38pXWdGmSeWyHJjKxSgRRFTCASqXrHEoAtgajpI1onIPr+zfDn+Hf1Ht8vTVT4ZKCzw4ZW9ZGMqnMDavzKtxO7vXBdXMaPsX4wAC9pAb0+5M4bCuAN/Yk3tBlvaJwlgLfGG1qHmGB+ifCXiPpbfIAV5UB94ZuG5+p4Q/dZmd6ahaYocp8LbGRLwdSYamVSMSnmI+FLlEcd+dbFjWsAW67907nX2se+AZxipWzLon7wfR54BW9MFgMQTgJiUNwdHgCOjai2pKcOugjgjGBe9re72RZaQF+uZ2U9A8pdYNaCEo3PG727be4sUfX8NvZFK0BtE2tgvk/P0p5BJCcIIOyPh1JfYaVJnXYUlXn2xIhZW2ZX4lvVDixfaxtu7fpWC+jR1WJ0QnAt3tCbWzTgdb2LWJmkITxcX6a+vONyRNm9ZkcL6EuJmTzoWdoA6+OJDQ5VAF9aj4q3gOsvDZW2f8eVWoSiuFEUUiIGPWrDi/CGKmic74Dn9LgtzFP/Q9hwD0TYcaPmhah9rLGVMlpA/xsrY9JO/L41epY2lnqNxSWIePG5iLHDBC7XAvoU6/jljbXTzPxe41B49VTVcfeXBn0K4K2XI1lYK95nzlJZ2LluIJ+EN1SO13U0EFIUqO5WM4tN8UOPm20OaVcceTzvHBVTUX735/hNd67v/ypq2tzVufVrvYYuMlIB3hhyTOp6xw+opkmksjPR62gB3dTuy33wWNusuEiqgq2zWRpZqaQctnm+4SiuVQ1UOh+16e7etrXtscEJoS6tr0ozXv15oBJ/6c8MVmCgWmo7OyrJCtrAokyIQ5mHeC8dgF9TCx6m5RDVtBxkJSiKaox2WbXuz/HP9uR7nqRe++cAUBNW4UybOswWL+os/t1L5b8nc/V13xuTiangt8t4Q68QzSXudR2HUDf3RjzTX9OEBSZ2lT1VX+PP8Uf8Of6nqLcLT/fn+AsaHhfMy14L/PxB5AROrn7yM4DeSkF6P2W5cY1tbHR2fwpCIEQF8tN4Q3dsJZD3kGBe9jzEYHYdcOCeCOR6lHuA/qAkI1YVl7IXhHJToQX0Ii2g65aQ/iBm1ybqkyE0Dd6QYY+PVAGYhjIUSy1I1CPf6xpK/ax7a8cyb2gp9SrSAXhd/azjwwhtSmSrc/YOcxEr+sa4H+F4VeYyjNScktL7gAl9qmvmvb9mHS7D2Kb925/jX4JVV9liOeJ9iybfuc6T7znWUmNHi3Q0lenkRuvazwCjtYCuI1SE9wEHawF9l+Mwm5hpADMOUHnpNDGc2UyGAYkmTJvZU4nKuTX+HL8V1hUqQazunrRl1ly6IRVsBgxcZg6zEhv9DhDMy44YVV3OPWZ6MnYDVrWKXz01My0NwF0bJllUQD3enetT3bm+nn3V5dcALDY7zUhSqvIBbIsjqlGjothMEtJr20dvuq+6st8fK1edN7Og4C4FjlWdxkfx6TVmm76lpAwrOuP+S2yYqkKVqpYgwiqvB46ywnhaCjMRviwZCA3NAGv7bpm6/Dn++xEx2+2wZEBC5ufYErYQJ+t+PVD9QgvoC3dGLb5TeEMhhNbnVKA33tBIa9s+oSlWylFuRuTm/Xk7xzwJHLfAdJ82KdI/cpRtrm1s3P2xE4MQwmnEjQi+b1phEEMwL3spwibYJFgPSHQg/Kap2t1HvItQffcGcrSA3rB85R7jTImsr9xI16rNjksRxcfXAb9atuG3rcM+xBvalio02tce6msr+/CGmrcq0XbQArphaRyiJp1chDPVe1pAF6pmr+t74Jw7Nxf/987NxcOAWxGOJ43Zk2O5DKFaCwE5lqPN58BDCEejn2KOrWLrkqO7+5tW0CCXtBbQp1Ov8tun+HP8pZ58z2jgvCl91atu+c4oRsTrT1Pg+YhN6YGY7G2pPfGGAsD9w4DjX+j340tvRk4EkXN/vru+L4N52bO+GPFoNRD33YD2HcMJ67ABWk0NFcqm0sVmYmfEiuv6oaqwIKUrJa+b8eafKOaNCHMdtZ3sZqUSd/XhVS8/8ZTjrYzjhQ9iG+AKLaA/h9e1CKFRmYQ39G1VvicHUf1tFN7QHKLOjC0If46/2pPv+QFhSryX+ip5uz0hjC7orL/pvTG7nkSYXdb4c/zb0ijtPqIi2J5GljQJimnuPfOEO9dnQwwW7kHKIsY4H8WuGCBWY8cjkrpH7bBevKFH9trNSfYqG8/pOHrD3NRzYza9rgX0G/C6bkBkL9qEKBiysdEGvK6uCMeqCLAe4UV5Ad7Qp40ev5ewipdcBTi0gP6anqWlASV1ece9roOoX0lMRUxA2wMj8IYm7ur1rGIHnyBWryBUfzf7c/xvbPusfxeefE880MGf498t9bkn39N3mN+YfuNYIyGUSJmrApfl14CepSnVNrUsLmIk3nOpjeWiMAS5mzYzY+PFP35tHHkiQAoVzIm7ClUszDPxhtasHNGtoHxNfCcjXuHS4feyIT79UeAhMCv0uMseSlBqnkXEgp+LCOdJBC7BG/pwT/tkbxFN4hKzab0/x7/L+Z630fZtCJPJ50CeP8dv7OCUfwV7VSgDuHN9ByIM7D3OUH9/9UXn64uAr/GGVlnpzSYjnJcG4Q01eUEHScug+vb0I5b9kPFbdCUBDNAC+hy8rsmIuOPb8Ya278DndS2kvsi8UKMJ1WTLxus6lC0949cAPbdjU94unnyPA2GDX41wsgnv4BRJA667u2+nG/9n6KpJMkJdPBGhuleBFyMKXHynjbBdwVNVvfmdtevTx9Qe94E3fOklAMPVWbzvfAbTZInySKgXQPl1bTtWbnZecWrmE1lr4tqeH3O5d4LxF9yMMHf0jNleBbTFG9qyPEILxnr2iqgPRZrgz/Efu51TJDtgrwvlKO5cX8dgXvbWqkmR01rBG9ovZkX7LV6XslFPXrBhTkpWcmZ1Zedhm5OBdIQaWwXceEPbj3WtX1UDfIM3dGaz3nNT4nU9gbDLbgJOxhtqspAzye6hZ2lvIIrY64jY1zqnKgPmnnev/WFgmX/5yjuAS5Yb7Z8dUfP8nQDvOp7xH2Ob5QHewRu6KrZdd66vLyK/QjzCTDEimJddgtc1CLFAcVuH/oA3dDL/MDz5nk+oNyE9admGJbvJPhPKEglelytSo2xW7aaqqFyISFKSh6jaM2gHZ2PFud+EWG08boU6/HMQyU6Kd3eFLGla9CytB0J4tmqwywTO0wL6GAC8rkcR5RjfdVd9ElQwOiyLu+gURaELcArekK9h2+5cXxbC/+GHujwMoi0VYTs9DThnhxPRFogn39MbYfN2AYP9Of7mzs/wr0YKZcm+xet6DBGiNhHhEdsOyMEb+mB7p0kkzYGepY1E1GmvQeRJmAosq3PUA/C6RiKSqczGGxqI16UBCxAmlPT9cZLlyfekAon+HP8+c7T8tyCFsmTf4nUNRqxOoiwG+m4z8F8iaWb0LE3ZbriN0HCsRGSvegRhU30RkV3quG2eJ5HsBHscpyyR7CEzqXd6MoDrpUCW7Et2GP/qDRUiwnNA2J2j4VaN1YeXSHYJKZQl+xaRwvR0hG14ON7Q9uLcJZKWQmwBnGi+h+1V3JJIdgqpvpZIJJLdwevqy5Z13w/EG2o0valEsrPIlbJEIpHsHguoL1zwLk2X2lSyHyNXyhKJRLK7iLwKbbcq8yeR7CZSKEskEolE0kKQ6muJRCKRSFoIUihLJBKJRNJCkEJZIpFIJJIWghTKEolEIpG0EKRQlkgkEomkhSCFskQikUgkLQQplCUSiUQiaSFIoSyRSCQSSQtBCmWJRCKRSFoIUihLJBKJRNJCkEJZIpFIJJIWghTKEolEIpG0EKRQlkgkEomkhSCFskQikUgkLQQplCUSiUQiaSFIoSyRSCQSSQvh/wF8+02YkCKycQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 600x400 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig=plt.figure(dpi=100)\\n\",\n    \"plot_voltage_traces(mem_rec, spk_rec)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, our random initialization gives us some sporadic spiking. Thus far, we have only an input layer and a spiking layer, which should become our hidden layer. Next, we will have to add a readout layer to our network.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Adding a readout layer\\n\",\n    \"\\n\",\n    \"To use our network as a classifier, we need to have a readout layer on whose output we can define a cost function. There are several possibilities for doing this. For instance, we could count output layer spikes, or we could directly define an objective function on the membrane potential of the output neurons. Here we will follow the latter approach, but keep in mind that there are many other possibilities of defining an output layer and respective cost functions on them.\\n\",\n    \"\\n\",\n    \"In the following, we will build the output layer as a population of leaky integrator neurons. The reason for this choice is that leaky integration is the natural way of how neurons receive the spiking output of their brethren. Moreover, because we will need this code again, we combine our code from above plus the added readout layer into a single function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def run_snn(inputs):\\n\",\n    \"    h1 = torch.einsum(\\\"abc,cd->abd\\\", (inputs, w1))\\n\",\n    \"    syn = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\\n\",\n    \"    mem = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\\n\",\n    \"\\n\",\n    \"    mem_rec = []\\n\",\n    \"    spk_rec = []\\n\",\n    \"\\n\",\n    \"    # Compute hidden layer activity\\n\",\n    \"    for t in range(nb_steps):\\n\",\n    \"        mthr = mem-1.0\\n\",\n    \"        out = spike_fn(mthr)\\n\",\n    \"        rst = out.detach() # We do not want to backprop through the reset\\n\",\n    \"\\n\",\n    \"        new_syn = alpha*syn +h1[:,t]\\n\",\n    \"        new_mem = (beta*mem +syn)*(1.0-rst)\\n\",\n    \"\\n\",\n    \"        mem_rec.append(mem)\\n\",\n    \"        spk_rec.append(out)\\n\",\n    \"        \\n\",\n    \"        mem = new_mem\\n\",\n    \"        syn = new_syn\\n\",\n    \"\\n\",\n    \"    mem_rec = torch.stack(mem_rec,dim=1)\\n\",\n    \"    spk_rec = torch.stack(spk_rec,dim=1)\\n\",\n    \"\\n\",\n    \"    # Readout layer\\n\",\n    \"    h2= torch.einsum(\\\"abc,cd->abd\\\", (spk_rec, w2))\\n\",\n    \"    flt = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\\n\",\n    \"    out = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\\n\",\n    \"    out_rec = [out]\\n\",\n    \"    for t in range(nb_steps):\\n\",\n    \"        new_flt = alpha*flt +h2[:,t]\\n\",\n    \"        new_out = beta*out +flt\\n\",\n    \"\\n\",\n    \"        flt = new_flt\\n\",\n    \"        out = new_out\\n\",\n    \"\\n\",\n    \"        out_rec.append(out)\\n\",\n    \"\\n\",\n    \"    out_rec = torch.stack(out_rec,dim=1)\\n\",\n    \"    other_recs = [mem_rec, spk_rec]\\n\",\n    \"    return out_rec, other_recs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can now run this code and plot the output layer \\\"membrane potentials\\\" below. As desired, these potentials do not have spikes riding on them.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"out_rec,other_recs = run_snn(x_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 600x400 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig=plt.figure(dpi=100)\\n\",\n    \"plot_voltage_traces(out_rec)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"By preventing the output neurons from spiking themselves, we can define a relatively smooth objective on their membrane voltages directly. Specifically, we use the maximum voltage over time of each output unit\\n\",\n    \"$$\\\\hat U^\\\\mathrm{out}_i=\\\\max_t U^\\\\mathrm{out}_i(t)$$\\n\",\n    \"and then use this vector as input for either an argmax to compute the classification accuracy or as we will see below as input for a standard softmax function in conjunction with a negative log-likelihood loss for optimizing the weights in the network. \\n\",\n    \"\\n\",\n    \"Let us first compute the classification accuracy of this random network. We will see that this accuracy is somewhere around 50% as it should be since that corresponds to the chance level of our synthetic task.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy 0.516\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def print_classification_accuracy():\\n\",\n    \"    \\\"\\\"\\\" Dirty little helper function to compute classification accuracy. \\\"\\\"\\\"\\n\",\n    \"    output,_ = run_snn(x_data)\\n\",\n    \"    m,_= torch.max(output,1) # max over time\\n\",\n    \"    _,am=torch.max(m,1) # argmax over output units\\n\",\n    \"    acc = np.mean((y_data==am).detach().cpu().numpy()) # compare to labels\\n\",\n    \"    print(\\\"Accuracy %.3f\\\"%acc)\\n\",\n    \"    \\n\",\n    \"print_classification_accuracy()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Supervised learning\\n\",\n    \"\\n\",\n    \"So far, we have built the infrastructure to simulate our spiking neural network, but we have worked with purely random network weights thus far.\\n\",\n    \"The vanilla method to adjust network weights to decrease the specified objective is gradient descent. \\n\",\n    \"Machine learning libraries like Tensorflow and PyTorch make implementing gradient descent a breeze.\\n\",\n    \"We first perform gradient descent on the correct gradient and use this as a motivation for introducing surrogate gradients.\\n\",\n    \"Here we go.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Supervised learning with the true gradient\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"params = [w1,w2] # The paramters we want to optimize\\n\",\n    \"optimizer = torch.optim.Adam(params, lr=2e-3, betas=(0.9,0.999)) # The optimizer we are going to use\\n\",\n    \"\\n\",\n    \"log_softmax_fn = nn.LogSoftmax(dim=1) # The log softmax function across output units\\n\",\n    \"loss_fn = nn.NLLLoss() # The negative log likelihood loss function\\n\",\n    \"\\n\",\n    \"# The optimization loop\\n\",\n    \"loss_hist = []\\n\",\n    \"for e in range(1000):\\n\",\n    \"    # run the network and get output\\n\",\n    \"    output,_ = run_snn(x_data) \\n\",\n    \"    # compute the loss\\n\",\n    \"    m,_=torch.max(output,1)\\n\",\n    \"    log_p_y = log_softmax_fn(m) \\n\",\n    \"    loss_val = loss_fn(log_p_y, y_data)\\n\",\n    \"\\n\",\n    \"    # update the weights\\n\",\n    \"    optimizer.zero_grad()\\n\",\n    \"    loss_val.backward()\\n\",\n    \"    optimizer.step()\\n\",\n    \"    \\n\",\n    \"    # store loss value\\n\",\n    \"    loss_hist.append(loss_val.item())\\n\",\n    \"    \\n\",\n    \"loss_hist_true_grad = loss_hist # store for later use\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(loss_hist)\\n\",\n    \"plt.xlabel(\\\"Epoch\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"sns.despine()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy 0.512\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print_classification_accuracy()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We appreciate that loss decreases over iterations and converges towards a steady state. The classification accuracy, however, does not seem to improve dramatically throughout the optimization. What a shame! \\n\",\n    \"\\n\",\n    \"The underlying reason is that the nonlinearity of the hidden units have zero derivatives everywhere except at threshold crossings, where they become infinite. In practice that means that weight updates in the hidden layer vanish and the weights remain unmodified. By plotting the hidden layer activations and comparing them with what we have plotted before, we will see that these activations have not changed at all. Thus no learning happens in the hidden layer. The reason why the loss decreased initially during optimization is that the output layer weights could still change and allow for some improvement (even if it was very little).\\n\",\n    \"\\n\",\n    \"To improve performance, we need to get the hidden layer units to take part in learning. To achieve this, we will introduce a surrogate gradient in the next section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"output,other_recordings = run_snn(x_data)\\n\",\n    \"mem_rec, spk_rec = other_recordings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 600x400 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig=plt.figure(dpi=100)\\n\",\n    \"plot_voltage_traces(mem_rec, spk_rec)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 600x400 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig=plt.figure(dpi=100)\\n\",\n    \"plot_voltage_traces(output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Supervised learning with surrogate gradients\\n\",\n    \"\\n\",\n    \"In the last section, we saw that the hidden layer units did not participate.\\n\",\n    \"The underlying reason is that the partial derivative of the step function we used has a vanishing derivative everywhere (except at zero where it becomes infinite).\\n\",\n    \"\\n\",\n    \"Most conventional neural networks avoid this problem by choosing a nonlinearity with non-zero partial derivative. For instance, sigmoidal or tanh units were standard during the beginnings of neural networks research. Today, ReLUs are more common. Importantly, all these activation functions have substantial non-zero support, which allows gradients to flow (to a greater or lesser extent).\\n\",\n    \"\\n\",\n    \"What do we if we want to stick to our binary nonlinearity? There have been several approaches to tackle this problem. Here we use one such strategy which has been applied successfully to spiking neural networks: We use a surrogate gradient approach.\\n\",\n    \"\\n\",\n    \"The idea behind a surrogate gradient is dead simple. Instead of changing the nonlinearity itself, we only change the gradient. Thus we use a different \\\"surrogate\\\" gradient to optimize parameters that would otherwise have a vanishing gradient.\\n\",\n    \"\\n\",\n    \"<img src=\\\"figures/surrgrad/surrgrad.png\\\" width=\\\"450\\\">\\n\",\n    \"Specifically, we use the partial derivative of a function which to some extent approximates the stepfunction $\\\\Theta(x)$.\\n\",\n    \"In what follows, chiefly, we will use (up to rescaling) the partial derivative of a fast sigmoid function $\\\\sigma(x)$. \\n\",\n    \"While $\\\\Theta$ is invariant to multiplicative rescaling, $\\\\sigma$ isn't. Thus we have to introduce a scale parameter.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class SurrGradSpike(torch.autograd.Function):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Here we implement our spiking nonlinearity which also implements \\n\",\n    \"    the surrogate gradient. By subclassing torch.autograd.Function, \\n\",\n    \"    we will be able to use all of PyTorch's autograd functionality.\\n\",\n    \"    Here we use the normalized negative part of a fast sigmoid \\n\",\n    \"    as this was done in Zenke & Ganguli (2018).\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    scale = 100.0 # controls steepness of surrogate gradient\\n\",\n    \"\\n\",\n    \"    @staticmethod\\n\",\n    \"    def forward(ctx, input):\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        In the forward pass we compute a step function of the input Tensor\\n\",\n    \"        and return it. ctx is a context object that we use to stash information which \\n\",\n    \"        we need to later backpropagate our error signals. To achieve this we use the \\n\",\n    \"        ctx.save_for_backward method.\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        ctx.save_for_backward(input)\\n\",\n    \"        out = torch.zeros_like(input)\\n\",\n    \"        out[input > 0] = 1.0\\n\",\n    \"        return out\\n\",\n    \"\\n\",\n    \"    @staticmethod\\n\",\n    \"    def backward(ctx, grad_output):\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        In the backward pass we receive a Tensor we need to compute the \\n\",\n    \"        surrogate gradient of the loss with respect to the input. \\n\",\n    \"        Here we use the normalized negative part of a fast sigmoid \\n\",\n    \"        as this was done in Zenke & Ganguli (2018).\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        input, = ctx.saved_tensors\\n\",\n    \"        grad_input = grad_output.clone()\\n\",\n    \"        grad = grad_input/(SurrGradSpike.scale*torch.abs(input)+1.0)**2\\n\",\n    \"        return grad\\n\",\n    \"    \\n\",\n    \"# here we overwrite our naive spike function by the \\\"SurrGradSpike\\\" nonlinearity which implements a surrogate gradient\\n\",\n    \"spike_fn  = SurrGradSpike.apply\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"init done\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# The following lines will reinitialize the weights\\n\",\n    \"torch.nn.init.normal_(w1, mean=0.0, std=weight_scale/np.sqrt(nb_inputs))\\n\",\n    \"torch.nn.init.normal_(w2, mean=0.0, std=weight_scale/np.sqrt(nb_hidden))\\n\",\n    \"print(\\\"init done\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"params = [w1,w2]\\n\",\n    \"optimizer = torch.optim.Adam(params, lr=2e-3, betas=(0.9,0.999))\\n\",\n    \"\\n\",\n    \"log_softmax_fn = nn.LogSoftmax(dim=1)\\n\",\n    \"loss_fn = nn.NLLLoss()\\n\",\n    \"\\n\",\n    \"loss_hist = []\\n\",\n    \"for e in range(1000):\\n\",\n    \"    output,_ = run_snn(x_data)\\n\",\n    \"    m,_=torch.max(output,1)\\n\",\n    \"    log_p_y = log_softmax_fn(m)\\n\",\n    \"    loss_val = loss_fn(log_p_y, y_data)\\n\",\n    \"\\n\",\n    \"    optimizer.zero_grad()\\n\",\n    \"    loss_val.backward()\\n\",\n    \"    optimizer.step()\\n\",\n    \"    loss_hist.append(loss_val.item())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 495x300 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(3.3,2),dpi=150)\\n\",\n    \"plt.plot(loss_hist_true_grad, label=\\\"True gradient\\\")\\n\",\n    \"plt.plot(loss_hist, label=\\\"Surrogate gradient\\\")\\n\",\n    \"plt.xlabel(\\\"Epoch\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"sns.despine()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"output,other_recordings = run_snn(x_data)\\n\",\n    \"mem_rec, spk_rec = other_recordings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 600x400 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig=plt.figure(dpi=100)\\n\",\n    \"plot_voltage_traces(mem_rec, spk_rec)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 600x400 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig=plt.figure(dpi=100)\\n\",\n    \"plot_voltage_traces(output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy 0.859375\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"output,_ = run_snn(x_data)\\n\",\n    \"m,_=torch.max(output,1)\\n\",\n    \"\\n\",\n    \"# Compute training accuracy\\n\",\n    \"_,am=torch.max(m,1)\\n\",\n    \"acc = np.mean((y_data==am).detach().cpu().numpy())\\n\",\n    \"print(\\\"Accuracy %f\\\"%acc)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a rel=\\\"license\\\" href=\\\"http://creativecommons.org/licenses/by/4.0/\\\"><img alt=\\\"Creative Commons License\\\" style=\\\"border-width:0\\\" src=\\\"https://i.creativecommons.org/l/by/4.0/88x31.png\\\" /></a><br />This work is licensed under a <a rel=\\\"license\\\" href=\\\"http://creativecommons.org/licenses/by/4.0/\\\">Creative Commons Attribution 4.0 International License</a>.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/SpyTorchTutorial2.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tutorial 2: Training a spiking neural network on a simple vision dataset\\n\",\n    \"\\n\",\n    \"Friedemann Zenke (https://fzenke.net)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> For more details on surrogate gradient learning, please see: \\n\",\n    \"> Neftci, E.O., Mostafa, H., and Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks.\\n\",\n    \"> https://arxiv.org/abs/1901.09948\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In Tutorial 1, we have seen how to train a simple multi-layer spiking neural network on a small synthetic dataset. In this tutorial, we will apply what we have learned so far to a slightly larger dataset.\\n\",\n    \"Concretely, we will use the [Fashion MNIST dataset](https://github.com/zalandoresearch/fashion-mnist). \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib.gridspec import GridSpec\\n\",\n    \"import seaborn as sns\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torchvision\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'1.7.0'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"torch.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# The coarse network structure is dicated by the Fashion MNIST dataset. \\n\",\n    \"nb_inputs  = 28*28\\n\",\n    \"nb_hidden  = 100\\n\",\n    \"nb_outputs = 10\\n\",\n    \"\\n\",\n    \"time_step = 1e-3\\n\",\n    \"nb_steps  = 100\\n\",\n    \"\\n\",\n    \"batch_size = 256\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dtype = torch.float\\n\",\n    \"\\n\",\n    \"# Check whether a GPU is available\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    device = torch.device(\\\"cuda\\\")     \\n\",\n    \"else:\\n\",\n    \"    device = torch.device(\\\"cpu\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Here we load the Dataset\\n\",\n    \"root = os.path.expanduser(\\\"~/data/datasets/torch/fashion-mnist\\\")\\n\",\n    \"train_dataset = torchvision.datasets.FashionMNIST(root, train=True, transform=None, target_transform=None, download=True)\\n\",\n    \"test_dataset = torchvision.datasets.FashionMNIST(root, train=False, transform=None, target_transform=None, download=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Standardize data\\n\",\n    \"# x_train = torch.tensor(train_dataset.train_data, device=device, dtype=dtype)\\n\",\n    \"x_train = np.array(train_dataset.data, dtype=np.float)\\n\",\n    \"x_train = x_train.reshape(x_train.shape[0],-1)/255\\n\",\n    \"# x_test = torch.tensor(test_dataset.test_data, device=device, dtype=dtype)\\n\",\n    \"x_test = np.array(test_dataset.data, dtype=np.float)\\n\",\n    \"x_test = x_test.reshape(x_test.shape[0],-1)/255\\n\",\n    \"\\n\",\n    \"# y_train = torch.tensor(train_dataset.train_labels, device=device, dtype=dtype)\\n\",\n    \"# y_test  = torch.tensor(test_dataset.test_labels, device=device, dtype=dtype)\\n\",\n    \"y_train = np.array(train_dataset.targets, dtype=np.int)\\n\",\n    \"y_test  = np.array(test_dataset.targets, dtype=np.int)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(-0.5, 27.5, 27.5, -0.5)\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Here we plot one of the raw data points as an example\\n\",\n    \"data_id = 1\\n\",\n    \"plt.imshow(x_train[data_id].reshape(28,28), cmap=plt.cm.gray_r)\\n\",\n    \"plt.axis(\\\"off\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Since we are working with spiking neural networks, we ideally want to use a temporal code to make use of spike timing. To that end, we will use a spike latency code to feed spikes to our network.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def current2firing_time(x, tau=20, thr=0.2, tmax=1.0, epsilon=1e-7):\\n\",\n    \"    \\\"\\\"\\\" Computes first firing time latency for a current input x assuming the charge time of a current based LIF neuron.\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"    x -- The \\\"current\\\" values\\n\",\n    \"\\n\",\n    \"    Keyword args:\\n\",\n    \"    tau -- The membrane time constant of the LIF neuron to be charged\\n\",\n    \"    thr -- The firing threshold value \\n\",\n    \"    tmax -- The maximum time returned \\n\",\n    \"    epsilon -- A generic (small) epsilon > 0\\n\",\n    \"\\n\",\n    \"    Returns:\\n\",\n    \"    Time to first spike for each \\\"current\\\" x\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    idx = x<thr\\n\",\n    \"    x = np.clip(x,thr+epsilon,1e9)\\n\",\n    \"    T = tau*np.log(x/(x-thr))\\n\",\n    \"    T[idx] = tmax\\n\",\n    \"    return T\\n\",\n    \" \\n\",\n    \"\\n\",\n    \"def sparse_data_generator(X, y, batch_size, nb_steps, nb_units, shuffle=True ):\\n\",\n    \"    \\\"\\\"\\\" This generator takes datasets in analog format and generates spiking network input as sparse tensors. \\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"        X: The data ( sample x event x 2 ) the last dim holds (time,neuron) tuples\\n\",\n    \"        y: The labels\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    labels_ = np.array(y,dtype=np.int)\\n\",\n    \"    number_of_batches = len(X)//batch_size\\n\",\n    \"    sample_index = np.arange(len(X))\\n\",\n    \"\\n\",\n    \"    # compute discrete firing times\\n\",\n    \"    tau_eff = 20e-3/time_step\\n\",\n    \"    firing_times = np.array(current2firing_time(X, tau=tau_eff, tmax=nb_steps), dtype=np.int)\\n\",\n    \"    unit_numbers = np.arange(nb_units)\\n\",\n    \"\\n\",\n    \"    if shuffle:\\n\",\n    \"        np.random.shuffle(sample_index)\\n\",\n    \"\\n\",\n    \"    total_batch_count = 0\\n\",\n    \"    counter = 0\\n\",\n    \"    while counter<number_of_batches:\\n\",\n    \"        batch_index = sample_index[batch_size*counter:batch_size*(counter+1)]\\n\",\n    \"\\n\",\n    \"        coo = [ [] for i in range(3) ]\\n\",\n    \"        for bc,idx in enumerate(batch_index):\\n\",\n    \"            c = firing_times[idx]<nb_steps\\n\",\n    \"            times, units = firing_times[idx][c], unit_numbers[c]\\n\",\n    \"\\n\",\n    \"            batch = [bc for _ in range(len(times))]\\n\",\n    \"            coo[0].extend(batch)\\n\",\n    \"            coo[1].extend(times)\\n\",\n    \"            coo[2].extend(units)\\n\",\n    \"\\n\",\n    \"        i = torch.LongTensor(coo).to(device)\\n\",\n    \"        v = torch.FloatTensor(np.ones(len(coo[0]))).to(device)\\n\",\n    \"    \\n\",\n    \"        X_batch = torch.sparse.FloatTensor(i, v, torch.Size([batch_size,nb_steps,nb_units])).to(device)\\n\",\n    \"        y_batch = torch.tensor(labels_[batch_index],device=device)\\n\",\n    \"\\n\",\n    \"        yield X_batch.to(device=device), y_batch.to(device=device)\\n\",\n    \"\\n\",\n    \"        counter += 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setup of the spiking network model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tau_mem = 10e-3\\n\",\n    \"tau_syn = 5e-3\\n\",\n    \"\\n\",\n    \"alpha   = float(np.exp(-time_step/tau_syn))\\n\",\n    \"beta    = float(np.exp(-time_step/tau_mem))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"init done\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"weight_scale = 7*(1.0-beta) # this should give us some spikes to begin with\\n\",\n    \"\\n\",\n    \"w1 = torch.empty((nb_inputs, nb_hidden),  device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"torch.nn.init.normal_(w1, mean=0.0, std=weight_scale/np.sqrt(nb_inputs))\\n\",\n    \"\\n\",\n    \"w2 = torch.empty((nb_hidden, nb_outputs), device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"torch.nn.init.normal_(w2, mean=0.0, std=weight_scale/np.sqrt(nb_hidden))\\n\",\n    \"\\n\",\n    \"print(\\\"init done\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_voltage_traces(mem, spk=None, dim=(3,5), spike_height=5):\\n\",\n    \"    gs=GridSpec(*dim)\\n\",\n    \"    if spk is not None:\\n\",\n    \"        dat = 1.0*mem\\n\",\n    \"        dat[spk>0.0] = spike_height\\n\",\n    \"        dat = dat.detach().cpu().numpy()\\n\",\n    \"    else:\\n\",\n    \"        dat = mem.detach().cpu().numpy()\\n\",\n    \"    for i in range(np.prod(dim)):\\n\",\n    \"        if i==0: a0=ax=plt.subplot(gs[i])\\n\",\n    \"        else: ax=plt.subplot(gs[i],sharey=a0)\\n\",\n    \"        ax.plot(dat[i])\\n\",\n    \"        ax.axis(\\\"off\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can now run this code and plot the output layer \\\"membrane potentials\\\" below. As desired, these potentials do not have spikes riding on them.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the network\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class SurrGradSpike(torch.autograd.Function):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Here we implement our spiking nonlinearity which also implements \\n\",\n    \"    the surrogate gradient. By subclassing torch.autograd.Function, \\n\",\n    \"    we will be able to use all of PyTorch's autograd functionality.\\n\",\n    \"    Here we use the normalized negative part of a fast sigmoid \\n\",\n    \"    as this was done in Zenke & Ganguli (2018).\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    scale = 100.0 # controls steepness of surrogate gradient\\n\",\n    \"\\n\",\n    \"    @staticmethod\\n\",\n    \"    def forward(ctx, input):\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        In the forward pass we compute a step function of the input Tensor\\n\",\n    \"        and return it. ctx is a context object that we use to stash information which \\n\",\n    \"        we need to later backpropagate our error signals. To achieve this we use the \\n\",\n    \"        ctx.save_for_backward method.\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        ctx.save_for_backward(input)\\n\",\n    \"        out = torch.zeros_like(input)\\n\",\n    \"        out[input > 0] = 1.0\\n\",\n    \"        return out\\n\",\n    \"\\n\",\n    \"    @staticmethod\\n\",\n    \"    def backward(ctx, grad_output):\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        In the backward pass we receive a Tensor we need to compute the \\n\",\n    \"        surrogate gradient of the loss with respect to the input. \\n\",\n    \"        Here we use the normalized negative part of a fast sigmoid \\n\",\n    \"        as this was done in Zenke & Ganguli (2018).\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        input, = ctx.saved_tensors\\n\",\n    \"        grad_input = grad_output.clone()\\n\",\n    \"        grad = grad_input/(SurrGradSpike.scale*torch.abs(input)+1.0)**2\\n\",\n    \"        return grad\\n\",\n    \"    \\n\",\n    \"# here we overwrite our naive spike function by the \\\"SurrGradSpike\\\" nonlinearity which implements a surrogate gradient\\n\",\n    \"spike_fn  = SurrGradSpike.apply\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def run_snn(inputs):\\n\",\n    \"    h1 = torch.einsum(\\\"abc,cd->abd\\\", (inputs, w1))\\n\",\n    \"    syn = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\\n\",\n    \"    mem = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\\n\",\n    \"\\n\",\n    \"    mem_rec = []\\n\",\n    \"    spk_rec = []\\n\",\n    \"\\n\",\n    \"    # Compute hidden layer activity\\n\",\n    \"    for t in range(nb_steps):\\n\",\n    \"        mthr = mem-1.0\\n\",\n    \"        out = spike_fn(mthr)\\n\",\n    \"        rst = out.detach() # We do not want to backprop through the reset\\n\",\n    \"\\n\",\n    \"        new_syn = alpha*syn +h1[:,t]\\n\",\n    \"        new_mem = (beta*mem +syn)*(1.0-rst)\\n\",\n    \"\\n\",\n    \"        mem_rec.append(mem)\\n\",\n    \"        spk_rec.append(out)\\n\",\n    \"        \\n\",\n    \"        mem = new_mem\\n\",\n    \"        syn = new_syn\\n\",\n    \"\\n\",\n    \"    mem_rec = torch.stack(mem_rec,dim=1)\\n\",\n    \"    spk_rec = torch.stack(spk_rec,dim=1)\\n\",\n    \"\\n\",\n    \"    # Readout layer\\n\",\n    \"    h2= torch.einsum(\\\"abc,cd->abd\\\", (spk_rec, w2))\\n\",\n    \"    flt = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\\n\",\n    \"    out = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\\n\",\n    \"    out_rec = [out]\\n\",\n    \"    for t in range(nb_steps):\\n\",\n    \"        new_flt = alpha*flt +h2[:,t]\\n\",\n    \"        new_out = beta*out +flt\\n\",\n    \"\\n\",\n    \"        flt = new_flt\\n\",\n    \"        out = new_out\\n\",\n    \"\\n\",\n    \"        out_rec.append(out)\\n\",\n    \"\\n\",\n    \"    out_rec = torch.stack(out_rec,dim=1)\\n\",\n    \"    other_recs = [mem_rec, spk_rec]\\n\",\n    \"    return out_rec, other_recs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(x_data, y_data, lr=2e-3, nb_epochs=10):\\n\",\n    \"    params = [w1,w2]\\n\",\n    \"    optimizer = torch.optim.Adam(params, lr=lr, betas=(0.9,0.999))\\n\",\n    \"\\n\",\n    \"    log_softmax_fn = nn.LogSoftmax(dim=1)\\n\",\n    \"    loss_fn = nn.NLLLoss()\\n\",\n    \"    \\n\",\n    \"    loss_hist = []\\n\",\n    \"    for e in range(nb_epochs):\\n\",\n    \"        local_loss = []\\n\",\n    \"        for x_local, y_local in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs):\\n\",\n    \"            output,_ = run_snn(x_local.to_dense())\\n\",\n    \"            m,_=torch.max(output,1)\\n\",\n    \"            log_p_y = log_softmax_fn(m)\\n\",\n    \"            loss_val = loss_fn(log_p_y, y_local)\\n\",\n    \"\\n\",\n    \"            optimizer.zero_grad()\\n\",\n    \"            loss_val.backward()\\n\",\n    \"            optimizer.step()\\n\",\n    \"            local_loss.append(loss_val.item())\\n\",\n    \"        mean_loss = np.mean(local_loss)\\n\",\n    \"        print(\\\"Epoch %i: loss=%.5f\\\"%(e+1,mean_loss))\\n\",\n    \"        loss_hist.append(mean_loss)\\n\",\n    \"        \\n\",\n    \"    return loss_hist\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"def compute_classification_accuracy(x_data, y_data):\\n\",\n    \"    \\\"\\\"\\\" Computes classification accuracy on supplied data in batches. \\\"\\\"\\\"\\n\",\n    \"    accs = []\\n\",\n    \"    for x_local, y_local in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs, shuffle=False):\\n\",\n    \"        output,_ = run_snn(x_local.to_dense())\\n\",\n    \"        m,_= torch.max(output,1) # max over time\\n\",\n    \"        _,am=torch.max(m,1)      # argmax over output units\\n\",\n    \"        tmp = np.mean((y_local==am).detach().cpu().numpy()) # compare to labels\\n\",\n    \"        accs.append(tmp)\\n\",\n    \"    return np.mean(accs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1: loss=0.89463\\n\",\n      \"Epoch 2: loss=0.53745\\n\",\n      \"Epoch 3: loss=0.48071\\n\",\n      \"Epoch 4: loss=0.44689\\n\",\n      \"Epoch 5: loss=0.42401\\n\",\n      \"Epoch 6: loss=0.40686\\n\",\n      \"Epoch 7: loss=0.39227\\n\",\n      \"Epoch 8: loss=0.37885\\n\",\n      \"Epoch 9: loss=0.36825\\n\",\n      \"Epoch 10: loss=0.36175\\n\",\n      \"Epoch 11: loss=0.35250\\n\",\n      \"Epoch 12: loss=0.34323\\n\",\n      \"Epoch 13: loss=0.33708\\n\",\n      \"Epoch 14: loss=0.33294\\n\",\n      \"Epoch 15: loss=0.32420\\n\",\n      \"Epoch 16: loss=0.31920\\n\",\n      \"Epoch 17: loss=0.31117\\n\",\n      \"Epoch 18: loss=0.30753\\n\",\n      \"Epoch 19: loss=0.30294\\n\",\n      \"Epoch 20: loss=0.29746\\n\",\n      \"Epoch 21: loss=0.29385\\n\",\n      \"Epoch 22: loss=0.28760\\n\",\n      \"Epoch 23: loss=0.28413\\n\",\n      \"Epoch 24: loss=0.27772\\n\",\n      \"Epoch 25: loss=0.27499\\n\",\n      \"Epoch 26: loss=0.27133\\n\",\n      \"Epoch 27: loss=0.26617\\n\",\n      \"Epoch 28: loss=0.26376\\n\",\n      \"Epoch 29: loss=0.26041\\n\",\n      \"Epoch 30: loss=0.25375\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"loss_hist = train(x_train, y_train, lr=2e-4, nb_epochs=30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 495x300 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(3.3,2),dpi=150)\\n\",\n    \"plt.plot(loss_hist)\\n\",\n    \"plt.xlabel(\\\"Epoch\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"sns.despine()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training accuracy: 0.912\\n\",\n      \"Test accuracy: 0.863\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Training accuracy: %.3f\\\"%(compute_classification_accuracy(x_train,y_train)))\\n\",\n    \"print(\\\"Test accuracy: %.3f\\\"%(compute_classification_accuracy(x_test,y_test)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_mini_batch(x_data, y_data, shuffle=False):\\n\",\n    \"    for ret in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs, shuffle=shuffle):\\n\",\n    \"        return ret \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x_batch, y_batch = get_mini_batch(x_test, y_test)\\n\",\n    \"output, other_recordings = run_snn(x_batch.to_dense())\\n\",\n    \"mem_rec, spk_rec = other_recordings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 600x400 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig=plt.figure(dpi=100)\\n\",\n    \"plot_voltage_traces(mem_rec, spk_rec)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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BPS1h4TFLdxmd+fi8btp0pnw8cLqVsm9cSOhsYhlIdLUbdjz8AX7zTEDRRmXMMVIzyzcADKNVrCnB9j57BgU/XCGXF3ah7NoWOga3Nz3O8tfwSoDw5RQOutbb90xLEW7DWc+mo734VsLg8OWX7a343yhP7BOy45Z/jEIClGLjiv9ogBE7g+7JpZYsKsouOKcguuroguyitILsotSC76PyC7KLhZdPK6lDauedR76iXz0y5bpRAPAZwCx7TDcfQi1NwdptQTiwujUUZ8Rm9aW2atfnNnbXPz8p052dlbhOT+fQtN86dULP6fYCa2H6/P/GVd22Hl11gVYcaAhAXiq4CHgSo9Jv+gMSBCrXpn1OYcRBwClCLSrCw4J2GoJMOT9+HPih/8ig64sjvSZ2ROhyb3aU9TnyLUC7ILoopyC46syC76MSC7CJHeXKKuzw5JbE8OWVkeXLK5PLklAGdHkn3rqRDUDyOHhvZvV3vBajY7mOstS+t5QmoyIQmdlJT2VJl34qqlvYNEMP2ntm6txo1YAV4BD22X1d3vxeRCrCCEO5+8wYBjKk9bHZBdtEsVJjaMyhNxkKUbFhSkF30dPa8x1JR6TZnAi7gjVtG/OMdYO0QNC2PCMKUZqNXFtDpzpny8QBCmtURwcCx1rb3tm+Un5U5PD8r832gFWjNz8p8Kz8rc4t340HrVl6Q0FjXEnI42RwZ89HU51+1Vak7p11wtry87IGDgLGGlL4KnxmH8jS94qG4NsfI3NknPxTXVrfWYRyDCpOKBma90xDchDIvaMD/3qp49EvUSy0KeM5OKLLbbJkpF2QXiYLsoluBjcD7wGfhrRuWGJq7FlXBayWqEMWG8uSUV8qTU4Z1crz7gNWo+/tg93f/gGciSqA201E2s12w/i+lorxlZzumVJTLlIryT1BCvBDlAPZkeXLK9K2a/R1VF34QO1HF2gCWUF4duRjhaIuZsPHoxlOWTctFzYRDKIHcjCodWwd4UDnGf8ie99gXV8/PXxgWjJgLRJVHrPjf8rA11wGtR+HkQSWYHxuZO/uUfXFi3Ul3vmTPAxjSULse5UDxha7rW7wg87Myx+RnZeagPILPtNq4rf2+zc/K/Ft+Vqbr4Ttu94/YXHOBKxRic3RcfENEVFE39vlAZ6S1rHKg/RZgbUBqBrBZMx9+KK6tAFgDFAPf/zc6sPjh2Lb6zZr5Fcoz/r/ve4NfowRxrEe6P5628Rwdpa6bgh2j+fPosS4su/7i1imbUPHGDwFhwgytFaYh2yIGjSk99ProNk9/UI5cq1HP4sXA0vLklI/Kk1Pu2mLT1L0tKHUqQA56rJ3YZde0z5K/QfeGypNT0uhQNT/Z+S7bklJRHkTNxO63Nv2jPDlF5VnQvX5UXK0BXIoeO61rut17qM4tSQASTCRrE2aR2DiakysvjhIIF2pyNjGnMOPwnMKM6JzCjDjUAOd81OwY4BiHdN7/+2//NnHcpklrgMQbRj8wGThNIpuPUILZ4VH25dR9cY7dRbcIZSuEKRPg4LUr2+NfnwHIz8oU+VmZT6NUe0+gcsquRtkfDkWpWDWU6rQoPytz8HtXXPrx6E1rnwSoHDj0hNNffO1xbDpjJECkEb5aSnkRwOqA6W4U5qbno/1/BaahZsXVqJGp2xCc81y0/7hFrlADIIKSp4sag++j7M5DL958xgvHNKX+zTr+31JnpJ7Ww+d0oDEecLUYcc3FjTmvAVcgpRyy7ssPT/78j0MOL31EOEJteGPHMO/ovzaXHJd3XdHJBSNRZoT5qNnCacA9QGV5cso95ckpHnTvJ3SUu3sJPXbcjl9tY9GumZtvLdvVnP9LqSj/fncPYs2a/0xHCNSD5ckpfylPThHo3q8B3dpeaMeS78ChAGuFD4ejldOWXCEFmoYSuufmFGZsk5c/pzAjlFOY8XZOYcbFqPjya4ClApEwZfnvhh2/8lcIKW44M+W69QJxertgfoiImEj4eGTu7DE79OAApbtmyonAYKQ0k+o3jrS2fZCflTkelbyivQzaN6ig/JOnz5xVNn3mrIXTZ87KAn6NSvd4AvBdflbmcZ9dem5OUt2G7xCCRUNG3XDOc/+9s5v6fiAzCuBXm09xCyGimg3Jcmnwn2j/QCmIRNlx0lFq0AGoB+evCGrfjwjGLQhTCcCaTO4vagx+KqUsB4beXZ19w+HNKe+gfi+vp85InbQvTu4AIdWQDmbV3y1BjAeqx1a++XTy0v+eIUDENq78Ymzl25cDJQgRFXRHPwW8UHRywTJgMnA2Soh8CYQBdwHflSenHIaKsZ0HxAHvo8cO2gfndyDQHnrzTXlySjTbzZItk8LQguyi+N05WEpF+T1A+/tGR3lnO1CRDbNRA6lZ9kBpG9IAVkZUcuSas4gIRQvUQP/KnMKMbcIEy5NTNEsztKY8OWVhxtycf2TMzUmd/MVt+Y5Q2+MAqTUnkrb2lDDgqTNTrptnCeamw3DyCJGJ/RCfjcydPXb7ThyICCn3JoyycxKLS88APnCHgmuv+HL2UODH6PJvbwQ+RLm6G8Dl02fOemlnx7AE+Fso+1AIuO3Nc64saIyOW7EpOj7JFQpy+Oql971z5WV3dfkJHKCkzkj9H5Jf/3fp32vjzagBi9oM/upqM9Y5zddRlbk+qMqbusMNt9LXPYjkmmP8Tib7lNk+XPDxKTHOoZoQB0lk8z+GzFhRFPvNIajEC6eXTStb0IOnd2Cgx96/tG3ynZ94bwHwjlv++jXDqotfQP3up6dUlD8MUJBd5EAJ2XtRg53FQFZOYcZPAOXJKQL4FUqbNAiVL/6usefUvOSKML9EaUUWA6dYdX9toD2TVwtKUI4rf3XIBSg7/FIgpejkgqkoR62DUNEFx+QUZuyWl7wVv9yejfBd4LcpF6+TKJ+ANFS8/xQrlK1PU51b8jJw2ezw7wmt36JdPiWnMGPO1u2s3/lTdHjGb0/b8tHnzl89/LR0E5MPkp9hTXz5f4DsD8qfTDaRH2mIfmsxuZPWzZWYZ1TlTf22206sB+iumXIaQL8Wb3vBibmoUIJw1Ox48q4EMsD0mbOWomIN/4eydz5ywbv/fvvEL2an92v21gSdLr4dmfzns16Y+Zau63YlHUVKSuto4s2oASEp+dgMsM5p3laVN/Xiqryp73cmkAGq8qY2VOVNvRbBWfM9oQ3vhwcIIWmTnDbbGxzeasrFAhF167ppqX+ouWitJrV4YG7qjNTzevb0Dggm/dR6OgCxDcvfH1Zd/BLqd1+ESmEKQE5hhpFTmPE3IAPl8DUR+LYgu+jmguwih6U6fR2V0P8tlBdq3vJ3E1+r+S7mWpTj3kRgHnpsr7Kp7SUjUAI5UDWn/3rgFoCNA9KeLzq54G6UMG1PatQf+LAgu2i3rl9KRfnjqFm3HxWe9k35q0PGAGegUnkOBb5Ejz25607ngGUSQERDe4QmD2wvkC1+gxLIJsrz/SzgRpQfRhkQPmbFO+mD188LaWictvRyBnvHXAZ8f2bKdU4NcZyBXDUUjaeI7H86rq9G5s4+oG383TVT/i9wyeFVFRuPWlWR4Gys+1P42hX3oTwZR0+fOWvlzxxiC/lZmQKlzstHqfMaWjwRf3ntV3+4YVN0/FiA0ZvW1kyoWX3G8zf+4ccuP5kDhNQZqU6gZfqqa9yntKaxKmBwo6P19c0OedHOhHFnjMydPRB4eoAhzj+r1c0gQ0MAEyO0hrFuRxzAandN44NDn4tZ4akGlWTkjrJpZa3dcFoHFnpsxIbAuLrX6/4ehpTGcfP/3OzxN8Sici3/LqWivLGz3QqyixJQcZlnWZvmA9flFGb8AFtmE5ejbMrRQMAZbvxr9Fkbz3C45BiUI9504OlfXKKwt6DHnonycv+p/NUhT0lEweLk3zZvSDw6aqtWBdb/2cAoE2l+Exb6d0l4qBVVatYF1KC8gz8Cvq7Km7rlupYnpxwNvIESwkHg/mEnbf531GD/m6j68SZKA/I3dG+gu095f6M6t2QwsM5E8qE3RBuhZqd0JuQUZmxTFbA8OWUgyou9H3BXSkX5fdt9LlCD1vtM4Tim7OBr2dz/IJBBVke8TNHEH0yfWxT8oeaiR8+qP+k5J+IkgGKCPI3/3WrMa6rypm7ombPuOrpLKC8Ajjj9p68ZtXk9kUtL0YwQwKfTZ8469Wd275T8rMyDUanwJgGYQix6b+q0lqVDxxyFEET4fYzbuGb22I3Vvy+49ebarjqXA4XUGanjkCx9rfwxooSLl/1+ozDMP/SX/CitoPzjHJLfHOV3Xn60zxnmQjDEJTg0wmG6hdBMpPwo7kvx6oAP2eiqW4VKvPC/smllfVco6LFTP264adYy30kM3PS9N3XRv2NR0QVHplSUh3a1a0F2kUD5WjyEErwSeBm4L6cwYylAeXLKcJSqzxLeclP/lObNAyY2J2suCaoU3nR07/wdvqCvoMfeAuQbQfFm6ezU45aPvSCxdsCh7Z+WosKcnskpzJBTpn/w68P8zpmjQg4BsCAsyFxPSE0dtqUalYf8paq8qe3mhQHAv7CiTIAqzWXeO+68mnTNwW+sbeWoiIX39zLd6gFFdW7JpcB/NtDK/AYXPmfzU9OfOGeHmOLy5JR/ohIT/QgcsbNnxBLOFxia68FFE68YUzvgEAASNsyj1vk2JQe1Ni4Z5nzgv8v/ER0uw3I1hNaK5DUC/vcIPFCDzK/Km9rcfWfctXS5UE4sLhVI2YAQMb9f9Ab96zYRXbcUV0SwSnPKjMvv+WK3Z8nbk5+V6UTFG/4V5bXNipHJNR+fdF7/pvAoF0B0W4scXrehZLB3859fzrm6pEtO6gAgdUbquYfXp719f801BKXkN2bz618+dNav9/a4I3NnRyeFtFuP9Dmnjw05Ij0CDg53MNStLB8GBl/E/MCHcV+yMGJZhSnMx4FXy6aV1e/tdx9oBO4e9OALm/59e1BGMOn7h4htrKoBzkipKP8RYGTu7AiUnXgiSnW6BjVTWAZUVOVN9RVkFw1FCeb2fO8SNaP7F/BhxtycIEoQPIRVplNosi1uTIszbnSrKywuhBDMQdmi3+9rMzXzrtgXa3+K/u2P3qlUjjkPw7ElG+NlOYUZ7QU+2geepZrkkDNaXQ0HBZ1xAF5hLvw4IvholcuMRDnenYkaJLVThvIgfvOy8o8qfrPkk4tQyV2sfMxyVWSi/6vBRzac5oo0+1v7fI+KZ34d3XvACIdfyurcz/6loV21zBdisU8CpOQUZlRs3aY8OeUolDOjE5iSUlG+Y6irHjsU5TvhAEabBkd5qyMmfO2fdkhl9CkDEBrOUCtJ1XMJbyzhuzFNfn9ScvE5ZI/pJ8PHAbQi+Zig72tCM+cRemB53tT93t7f5UJ54pufDKqLH1gD8Ly8BDfbvBPWAGdMyajcrVSbOyM/KzMelX3qeiA+6HDy3eEnywWpxwqfWz2EQpoMaqxv6dfSOC/G1/JGUt2mV5647Wbv3nzv/kzqjFT9b0v/9JfDjKEsCRlc6Ww5sSpvapcNSkbmznZO8jtuGhN03D48pA3s79CY4NFIcHW4JTRozXwd/SM/RFaEloWv/myde9NbKH+C8r4wg17yf2d/+6n35klufwPHz/sz/zz0glc/GHVsHeqFfDLKr2LwTnY3UDOr74CKST6HeazfdVa4VCo5Cy/wAfCJ298w7/h5fz5BIG+nI1kJrqgQUUN8RCYECB8QqHd6zPdQKtjPrWxUvZoN5w2rWbn24EHfTVIh9a3aqhah9Zt66xPnfrZ1u5G5sy01t9kUPuKZqedWnXlCknfC3QLhQSUyKgCefiiubS1KM/FbVNKLrZMXrQA+HdDaMP+hLwqSB7XWXy7ACgGV0hVlrIsd2ZoQOSjg8vQLoDloRTm7foiq57ysN86gF9354YZYMzLh6+YQK81A2a1Pnn7I1p+XJ6eEo7J4jQVeS6kov2jLh3rsMNRA6EY6bP87sM6fzGf111JnpWYQ0qBfXQUDahfi9FfQMmZU2/BB54l450BP+z5rMPgBY1MVZvFijP/+hDFnf5xBd7lQvirvzqdnHX3RNf3lJh41/4AQ0hCCzSinsgGoUePRUzIqd6nO2x3yszIjUdWnpgHH+50uFh18DAuTJ7EpbtushU4jRFxLUyDK17rREwqscJpmucsMLXYaRkVEwLckpq21+l83X2/sbZ/2Fbc/dPOiGzdfMBHg6UDLqpfcxqg9sSXDloclGjV6NYBN2+cIBrjsjx+lDzC1vyaGxDFJmuYa6dYY6tJwa9vq/RpFG9XOWta6a41ah3djvcu7rNFd/2Ojs/GnNWEbymtd9WuAjb3BHr30kAlRiw++xrs+5jBt8NrPWBqq5slDL+is6WqUYN0AjIiClBE4JsQhYj2ohyQMgQN1A5wmckhQaxsU1FzCxBWUEJIqHMFENpqwKLqlpmFA3eKhMY1VyR5/ndsdaMQdaEYzA7giQoTFhgiLCeGMMBodbnO5w21WOD3mIleksdjpMVej7Ke1B/qsuuacYfH1VRF13x10K97YMdSGfcfrh70IggqUo93BqExfv2kqz8tDa82MHP34Rs3VkADQv2Xo2lOXTnPG+QZtCTVrcXlrvJ5NSxo9tUs2hzWuXi6jB2wODZhkhuKOlka4W03iFO5QoGFq1bxNZ1R9HT28eWPiNp0T0roPQdwxIVyRBq5wo0lzy3JnmFHmDDcXC40qlKp8PbAJ3evr7mvW1Vh12TcCvO8NEpRcmlOY8crWbcqTU+5EJWVZB0xMqSj3oseORUWIHGsKEBKEegRWoX7uzcAcax8JJJpSpCzzTT6hrOXM/htCyVt/Ba5AI9HN1SSKAANjBxITl4SmObdps0G2UY3Rskk4q5sQlS2wpAVZ0YisWIyxZCXmpq19CXqKPRbK9/7jT1Wrhw3sNA+yRPBt//GixjGYZGMRU3+Y/8jhk96/dUpGpTmnaMxgVAhHnM/k2TvWhn8kEYegki30cyAjE1wysr/DdEc7cERoUoQJKVwC0ymQmkBqSFMDUwgMoZwppLU0XUHN6Wl0x4W1uOJcPkf0Zlei66f+h7MsZiKrIobj1zyddXkLDhnCY/jwmH7CZBC3GcAlQ7jMEE5p4JQGDmnikAYaJg5poiERUqJJiaD9Pwhrfcim+pYHrrsrapdfvIfk3Xfrc5Xjkn6//Xa35hFpwWrGat8Q9UFVMHyRNsfRLL5ExYwfCny64S+BlcYgTgHWYdIifGRqrQx2L9ccrjUi3tEgwoUPhApXxoyVPqO/bNCiTacIl2HCZbqFA6fQpKPd7ubTYmSzlkCb6C+EI55wzUm4puERAmWcEwjJlr/bkRJCmAQxCEhJo+agVTho0Zz4hUZA06QfB0HhICQ0DCHUf9TfptAwEUjUD6LjDoAU6nuiW/3m3y+/Y9snsQu4euajOzyomhBiqGcdp27eTOun8SwYcQwLzagvvyIUg0o3WItKCPLMF8SMQnmcnooq+LHHURBSSkJYAlqCIcFAYkjrgZBgShPMEJgGSANhGkgCCGcLhsOHLyxAq9ug1W3ic5v4nZKAE/wOScCBDDogqAkMDUJbHjyBKawHT6jrjQDTurfSusUSgZCSpy65ucsjPG55IS/UFO7p9LhJ9ZpIXZTKasPHq2n30xLmbQG2yRcuTTeBzZNxxpThCNu4ZTOoH9DwhoM4eP1kkrwT0La6NR4BHk3g1EykwweOIFIzpBASU5gCAdI6jGZKIv0hIvwGEYEQDoJIlx/h9COdQXAGkA4DHCGkZiC1EH4XtLodtLoctDkcBJwafocm/ZpGQNMIOgQhTWAIzVoK9QwIseVetK9L616MXlr979vufvjqrrz+dz/51+b1/WMjOvvMobmICUaLozYYLAz5qwdVjh+VU5ixZQJWnpziQQnaBJTz40voseHAPAmHVg/xyOWjIqVmygYpeN5wanNQiaaqp2RUbnEUm1M0xoMy3yRFtIbGUd9/ckvr0CNbg/0Gt8kYD0KoW6pePmhAhCaI1AQRQuAUgjbhotXhpk248WsO/DgJiPZ3joMgEEQQEuoZCwpJCIkhkAYCE4m0Rg7t11tKa2m9i4av2bj6zlvvH7kn13ePX1i1CbHD3ks4eZfVOcKkj4MqV/NC4+FHvlBZOklbk9oGEWf/tp//8UmRxt0ejatuHuS/aqlPwylgdJjJEJeJc69qfhgQF9yyNpTNHMIiAEw01svBrGYk6xjKBhKpZSB19MdLHAERhiGctDijaKHrZOjJ7vldXjwg6AnL2Nn1nyV9PMUbmNMMV2PIOCPmbccZUUUOAiNNmk81TjC2TjWhgYwAIwLaBpi0HdPZEfGghDpKoHYWedYqoIpwqgD142y1/revNxDPGoazniFsYhC1DKCBeLzE0UQ0baLTy7TXFWBGh1Z0S6jc7IGThSk6P7QZ9Thnn9LIb5YkIJHHArNLMT65nda/fUKMH7gdlYhi61FiDcq004KSqS1ASCKdAYj3YwwyhTHQiYj0SKfLiYYQAhfg2uoqSWESiNhAIHIdjZGbWRkjqQp3sy4snA3OKDZrMXhFDI0MoIUoDLHLx3+vr79Dhnhqbw/SCfOHjnescI7u/MMEuDXuI6gMsrku/b/Afz2D3x5qBmOPNlpHn+bwrEnSwmoJG7glOqcGlUFtPXA+giGr4xf1XxO32Dxt84mjT2g4+tDhwYEJ/WS4xym2Hge42/8QpsNHIHI9gYgaguGbCIZvJhRWTyjMi+FqwuduJugwWcdQ1jKUGgZTSwJ19KeBOBqJpZloQqLTtP57dR9yql/r8tzQS4cOipwb0/kLo50XlZEmacyhS17P6XCGA/X7TwDW1DwYWLSuaEx21OFxdwVdYogphBl0axogTIfoh4ooaM857ptTNOYd1Cz8BFQGSAdAa4QTIrwIvESiRmAtRFDNcNaSxEYSqWUA9fSz3jkxtBCJFL94vLjb9yTL/3Fnuex3yR4L5fj6Zu/x8Qt2kglHEGX4GLlqNSnrRlCS/PAJUuOb9k9fqgtjqT/EhfEBhrtNhru3nXCYEn9Asikg8foMZ6ApGBHeHPJE+UJh0X7DHRVpuh0DTA8DTA/xppuIHbovt7lcatSqRkqxwAQkUm5AsgFp/TOlpE1oNGpumjUXLZqbNs2JT3PhFw6CwkkQB0HNQQgNU2iEhAOJwEDN2CQdI1W2mrGN2lS31yr67XH5Az8d37xgxLZnLfgq6gj8wsNSmczBwbIa4ZKJjRcaNJ5nhHBaF8qE8G80hA/MaHCvEgGpsch/sLkxmCideGS0U8pEzZTxmkGUyyeFo0ViBrQ6I6B5ZVD4pCFC0gShpqkaAg2J5nN7wlvCIiNb3BER1eHDwsojJogVntGsdg+j2bF7Ax2HDOEx/bhkELcM4pQh9R9jOw2FiWZpKgBrffurDwNam7rFXndc8/dbZoTtLAsbwUZXAq/yW44ffg1Nm8cwtvZYDTj7MJx8QszZKNVp+9CoGOWMNS8pb/I2yT9SZ6Q6UDboC1ApBzsyRUkIk24iDA+x0s04j8Hw8AAxHqgOn0CFOIilJLOWE5E7GThsjZAmHny4ZQCXDOJCXXeHNDquu3Wtta20Q+rmq7+BLfei/REUWz47Ys8u7m5wUN1qOThi8w4vxuXhw9jgSORfCUfxyIDrGb7gD5dWekedG2w45ilUadIYMPxhie/NccV9MwXMgBCcVjatrMw6xL+sGuTnotSrEzsuFACGSbCmNa4y0NKv3NUWuyIyGLU2wgyv26G2bxAn5RxMGSexhBRWMWpnQncbhDQJk37cWPfDegYcW56Djt+/Q5rb3IfttXUAWjD0066/cc8ZVlsXOl5b0KnsMDSYH9GRcbTSNeHcIUXfXbouY9J/reiBOwHqLw99aEbzLSCao7YcSkMNSu9GzaZPR6WfHQVEAFnbfZ0XZQ7auJn+rV9yYvwiUhNWM2JQo4iL3Z1z0aShrrcMWtc7qH77MqTeN7T//jveORqmdb07fudb/raOa+kqiPG27LEfU5fblN/M/t+TabGD/uASgrfcr/GvMXO3+VxK1iR7jFv+kOAfhFJdAyxYtHnCj49+94dhJtopqOIHhwAchIMpOJmMi8GdaPn8AR+NOGiWGq0m+EyJX0LQCEhXqLbVYdbXumTTaqfwr9AcZoV0uSoCjsjKDcHxa4IyoimnMOOAtSNvzeGvz/pyXf+k49JWL2Xqii9en3jS7EV05OwFeNlZLV5I+Jurlo6samUpFeU+9NghqOxSV7Ktqm8ucBW6t7Kz79R1fQJwOVJe0hwWPnxJ4giWDUqiISJ6m3bCNIlpbQpEtTY1hPvb1rsD/ipHKLgMKRebQiwPuNyrN/VPXF9x3pQD1qY59flXIxcmjWkOOl3cI29nkFEZeL1yovsY75Fk1p8kHWwxuK9C3ZeXk/Imb/PbS52ROgDlH3EjHRW/2lkKlMVosurM2ED/ieFmarjmOORbcYyrhJNZROoOM9+oUBMD/HXE+ZuIbmsjwufH0xbC5Qu1ugPBqrCgf0lYKFiBpi1Dc1SjZiF1qIxtLbquH1BOSP933/2Z7xx1/HsNrjh+K//NYQ3l/HnBjZhyy+Dkc+DKqrypy1NnpPYDnGXTyrbor6tzSwahCoi0F/xoNrXgW96k4mX1w+ZEBSM2HIrgeOhUnbZRwpKvObb5k9AZoyod48YFtbBtRkXuYID41iZi21qI8bUQ5WsjIuDDE/B73UH/Sk8wsMRtBBdrQluOEMqu3HE/fAfC/dB1PXx9dHzL/HEHiX6RNZRrBxNv1Dcu+uL8wtWf9pvSVhs2yT/B/H7zjaFDEDjD/AaJG/2EtxlvV4yP/hQomZJRuXDrY84pGiOAo1AOYINR76WSW3hi4wYx+AJUDP8p7GgGWgMsRsplQzas0cavXnp0fKDtULfT7XRLk7BQEIdpGV5MwxChYJ0IhaqFaaxCyhUCuRJYITVnleEJr8bhbNZ1vdttzF0ulAuyi8JHOWXzIVFuTUoTr6OCd7R4fjTdLI1aiLd13EoZinkJpRYdiVLVjUV52mkA8QjOwsW5uAND0LboiaQRlMbmZaKxpYFV4Ums8wymfSrqkq3+SMfmbxDinUYj8X0T55Kcwoxe7/HbTtLH30wLudwv9G/2kvXtp9IUjqTJJ740ChVCNndKRuWzO+ykUhLehEp00G4jWoaqN/sB8HVnySh0XU9HxSWfVh8RxffDJ7B84FCkpp4JzTBkP2/t6oi25jkhh+v1dYnD59akp7Vtf5zexlH/m+1dPXBozLmBN4yLXP91fOh1fvNho/uofsFYjmk+pHhQsN8PH8R98conV8/dkgYwdUaqQBVQuAEVLtU+napH5YmfBXz+6LDWsUAOkNWGJ/xjzuQjpuIVHUqrAd4ahq1bYyb4fW0Jbc2RkQF/+0drUQk1ilC5s1cfCC/4PUXXdedPg0e0fDH+MHeSXEMeN7GifnTF3xb80Q+iCLijKm+qH7ZUMToSiJcYTc2Dvp/o9PX7Y2v8kkGt/ctkyFO/KhCxsRlhjkMlLdqaeuALVJKXBT7CSq8U/z3OFfDnBd1hWzyOoloaGb5hTSixrTk4qNkbHuNrbZ9JVaCer2Lga13XN9KLeODOO77wu8OOHxz/HQ+k3olPhPO3hjsZv3YpYRUajeeHJEKIhE1+Di5vQigHr2x07269IxKLS92oesu5QNJWH/1Ih2f7N7cV/rkOOD0UEZ0f6J840YjaavJsGj4tGPgWeNd0h72L0Jb1hMDdHboleUjhVbPfPiwm6tz2WNZ2mpDyz7SK79hxcqoBR+NcfxnutkNwjNBQujdpGr5QzY++0Op5cQ0t9Swdcz4NcWqCLTCMMK15ts+MfQz4rLfMen8JicWl/ZFmLULj0vkfM8S/cXYzUWfv9OWrx0YDr6DCPEC9rP8CfLqzMA1d19NQmdUyfE4X34yaSPngEVtsM7GNdStdoeA/a/sN+ndNelqn2at6M1NmzHxt0fAJFw5vXc0D4TcDLL17ree5RlPL26qZRDl8vY4SxlfQUWoQVBapAuA/jw5rDaFUqbcAx5kI5jKFmfzGaBbRDoCoZi+pFd8xbvWyisjo2IB0e9rDT/zAf1D1seftLy+c7ub/9L++8fIJmRcYDgf3GrmM1paBKiJx75SMSrM6t6Q/qkTgo0Ck6fCxNu1xWvvvMkqzDhVTW4wa2JRNyag0ARKLSw/WjNC/TIfzGABnMEDK8oVyzNoV3ycKEYnT1S6kG1D34nld17tcpbw/oev6hcBryBALjxzIV5EnkSE/4kpVKBAAYUqOW1CPx28+CVy/u6FhicWl6agEMO1a1hpUDP+MmvS0LRq9/KzMEabT9W9/QtKUUKwVLi6lFKFgsXS6/o4Qc3Rd73LzYlfQLUK5ILtoMvD5KLdGqgeE1qHFCSL9T+P/4jUCK87C5T0BZ8oIHNGDEckOREJ7O2kEfgxUvCcCK+YeIo0Aq0adxsrhZyGFE4ERBB6XOP6RU5hR0+UncIAy7r3PKpqiYiccv2whqetWAOTquv7gDg312BjgY1RucR9qtvzMLoRxNMrGlgNoK/sP4vOxh9LmUZPrmKaGr1vCI3PWnnrkd91wWgcMR73+Yerq/okLhTR5Knh5c7SrOUpK3vzzuvDiFlNMQdmGO4u99KMyRj3x6LDWn6w256HUcsMA1jM4+DC5DetE0kCA+IZajv2umOTKn74MDB7xcSg6/haEiEWlfSwA8nRdP+BSDO4tuq5f9knKES9XJiSRVbuUc/rf0f7Rlx7vqFpX66DMxMXTHJoRjsSketI/Nrf2X6ze2hKQzhK00GuoYhUNwBJgxZSMym2ejcTiUg24FSnvRwinMxhgUtk8Dl38zf+cCUnlZlj4HShvsGYgD3hc1/WmnrgG+wO6rn8KTNmombw5+QJiAl6em3dNA4cHAsIpwwZt9DeNrWp9AbgH3Rv8mcORWFwagyok0p4ZbAMqidRzNelp/q3b5mdlXhyKiv23b/DICOl0KZdo03wFh+NPuq7/4uRVPUWXh4tYfIE0P14Z4LRVbQGmLLudsSet/2Cd/w2XC3HK9XimXI/npE6+v0FKc6bv22d9obULrgXhCYRFsuSoq9kUlmI1ke9KHDfmFGas6qa+H7A4jNAsYMJGT7i1RT6g6/oPuq5/vKWRHhuGKjJ+NGoGcAa6d6fVnnRdPxmV3nSEIQTzk8ZRNlr5v4T526oDrrDLlp5z8ufdcT4HGt9ceEbZ2Pe/DDaHR7o+WXa2eX7KKyEhuOD+oW3HA9dMyah8N3VG6s0oZxc3KoHCh8Czjw5rBeWZeh3bembXvsplX73H+acixEBXwM8JCz7lsEVf12qmeWNz8qSJCPFXq+3XwBW6ru9Vcp4DnMWjatdRmZDEZ57hTFuRSf3oWQDH+2JX4otdSSBync/jHf2dd2hJCM04CQgA6QjmTZmy5GdnKYnFpTFI+R+EyEQIxlSVk/7VB0vjmr2XN0847HKzw5djNnCtrutru+9091veA6YMr/Pi8bXS6InljlF5fyw55/wX9/RA1ux4BtYAFXga+L+a9LRtnKjyszI1CQ8E+ifeHhg4FIQA01yKpv1Gv/feA6aiXbfMlAEKsoucwgz9JDXnhJFV73NE2Eza6sKLtcP/Ua6Fx7ePdhpRL6VS4JvmD27zS7/3n1hVpkLD+vP92BtolgMB2QziOuDl7etx2igGz/n+BKlpJRGtzdz31RMsVZqzdcCJuq4r1Y4e+wzKHtMIpKN7Oy36rut6GHAfKiRB+CDwSfIk99pB6rlwBQOPB13u27cfpfZ1Jr3x0Tdr+w06cuKqJdyo3fd/0UmtN6DsXkGUqvo/UzIqpeW8ciTKy/pIVB3ldttlEzC3jfDXr+WF4wzhvBZg+NoVnFn8BjHN3jmmw/nblvFpdwPZ1j7/AO7YX1VyPYWu6+EBh7PlxWPPECGHk8L5XpJdZaxNexy0Tq1bJnDllIzKF3bn+InFpYOR8kOEOMQZCjLli1mkVnxXGIwdcJt/yMgXUH4BJmqA9XBvtN3vDrquTwQWCdM0FvRLcHx76Ak4QsHP1p565Mm7e4zE4lKB8rV4BGXhXAFcXZOetkNKzvysTIeEZ/2Dhv0+2M8KcJDyXwhxo67rB1QSlm4TygAF2UUXAq85Qj6O/VrHHWzCGRFqSjxp4AdETtBcLP77ytcbN6DCPq5B/aDR3Cbi8ETmht8mgzJCoDxPz90+f6rNtiQWl4ZphtFqOhzawx/cwYaIlBYvMZGo1I1H6jxyIaoUpgTORPd+1NlxdF0fB7yJyoBES8AfmHXkFHd93AA002g1NUdWTXrarB46rQOKk//79s0Vg0c+HB7wkf3C3+4/4upF96BsuxdaTb5FqasPRkXqbc3XKPvnR5eJN+KA14ApSCmP+7ZIHPv9XDQpnzA8ETe3jpqYj/LSlqjZ2L+6/+wODHRdXz53fNqYisEjSatcxv1lPnNAZKJAyE/WHvboPW39llyN0lQsA16eklG5bHeOm1hcOgwpixFiTERrExd88LIcvGnt9U0pRxQCL6GyC/qBLF3X3+m2EzwA0HVdoOy9Cb76TZUvnn3FGNPhADijJj2t0/fO1ljmgYeBP1qbXgRyatLTdkiLmZ+VKSQ87U8YdnWw/5ZkDDfpuv7Y9m0PBLqrnnI7byKNHwynhyUTLpYSCLU6o6s/qL9o/XtfXlj1pvcbVIjIR8CvQBI7qpWwUxOZ47nTsATyF8CxtkD+eWrS0/zh/tZKgG8iJnIFMyNABoBJR1J6EUoNDZC3C4F8GMqp5WBMo7WlsZ43jzvTXR83AGEa1abmONIWyDtnSEPts65QkDa3h42Dhl8yJaMygHpZ/wUVhnYEcDxKILehBj9/tbYfOyWj8sPLxBujUBWfpmiG4T//w5fF8d8Vo0l5N3Bj66iJf0QJZIBptkDegdIJNasBKE9KChbXzNTeXvHkmyPuP+304y58/8spGZW/n5JReemUjMq/7IFAHgTMQYgxsY11XPb2M8bgTWsvnT5z1pOo+3cpKh3kBX1dIANYGoIvAKI0x/K0xVvSVfwvsbg0Y1f7Wt7V/6FDIP8f8PvOBLLFnwP9E7cWyFceqAIZulko5xRmmAjHlUBw04A0sfbYs/Od4cYaAMPvQJoCNIkrMkTcmBZGnbEJ/6Fja99vuSto4nKgwgZOzynMqOvOfvYmgg7XXIBl8WNoaRPiYJY4ATYy4NUQWgTwCSo4fwd0XT8BFQM4UPO3+Rq9dRGvnZpFS2QMSLlIao6ja9LT+rK98mf573VXNfVraWwD2Dhw6Mj8rEz3lIzK4JSMyntQGqHLgTuwwnGmZFT+akpGpT4lo/K7KRmVMrG49GTUjDnZGQxs/u2bT7nHrloCoE+fOeveppQjpgB/t77uFl3XX+rpczwAmJ/Q1IAwTdMfFu5qiO1PSAbPz8/KHPTzu+5IYnFpNCqkbFxMUz0Xv/tv4hrrr5g+c9aruq6fA/zZanq1ruvvd9VJ9AJKAEJRMZ7J33xC0rqVEpVA58PE4tJ7EotL47bfIbG4NBJ4F7gYNci5tCY97e816WmdqnTzszLPCUXF3hNI2BIZdZOu6891w7n0GN09U8Yq1P4QwNKwM6Z+fHThCFRx8KmDj66/Yvz5NS+PPXvjwsFHej8tc17w1PsNd0ZLNBeqMPz5OYUZB3yxgp4kEOb5HKBm4FC+2zzUfxLzNTd+VpHEW5zRXEfsZejeHeyOuq6fixLYMVpbc9uGUMjz2tRp0qc8rL9GiBNr0tPW9ezZHJjEtLWsA9gwcIhGR+gGUzIql0/JqHxhSkZl3pSMym+nZFRuY49PLC69EJV0f4Ar6K+46pVHIhM21wjgSeAeXdcTUTMIDRVe82jPnNEBx3yHNBnY3GAArBw2bgXqml246912JLG41IGK2z88vK2Zi2a9QEyz987pM2e9qOv6EOB5q+ljuq6/0EX97y18DYDDOcEdDCz99ewZIs67eT4qFv8u4KvE4tJJ7Y0Ti0sHon7/p6My9Z5dk572yo6HVeRnZQ43XWEvtQ0Z1b6p4ECeIbfT7ULZ4kGU80oycGpKRfm6lIry9+Nm1DzvuL/ht+jeQwtq3nrum+ZLrgIRBrwFXJRTmGE7Ee05CwA2DBzC4pZBoSij6ZHTKHkfpFzEhKjHuaJI1/XU9sa6rnt0Xb8Xdc09WmtzzXpJ+JtnXEbQ5RYoQX1KTXqara3YTWJ9LUsAagYMAcsu/3MkFpcehbJLaq5g4M3rZuRFRbc2eVDaoj82pRwB8Awqb3AZcH1fdSLaDb4DQoO9m10Ai8anbba2X5uflbmn77y/A2c5QkF+9cFLxHs3v4IKNxMoL+B+qMp3t3dR33sTC1FOb4mG2/Op0whxxczHl6BK7m5AFWP5MrG49IXE4tI3UQWLjkYlZ5lSk5724c4OnJ+VqUkhXmwbOioGhxOknIcq53vA0yNCOacwo5EOe+bDBdlFMe2fFWQX9SvILnoEFafpQhUQz8opzDhgUy7uY5Yi5YaQ00VNwrDIJ5ce+/UR+hdTQWShbJgHAx/ouv5HS/W5DqV+E47Wpi/NTdWJH6Sf356d6z9A5i5sOTadMKxu42eaabIxfiAVYw4+7efaJxaXjkCp7DxIOfvG5+6Lc4eCSagY2Uumz5wVQqnzzkZ5cV+m63qvz5D2S7GuzQ/D61SYds3AoSMlohlVreus3T1OYnHp71GJWzir+A0Gb1y7GLh6+sxZEuWUmom6H7/Tdd1+X22HrustqOxlBOMH1gM4TCO1Jj2tABVh8xEq4mAaKqHLAKv9cTXpafN/5vDXBvonnmSGR4GUjQhxsa7rPxvvfCDQUzNlUKkcN6ESI1QUZBc9WZBdVIjy0LvJavMP4LKcwoxecXH3BTXpaRIhPgdYrdQ6VwDouv4aKp3pUpT54FHgN0A8sMbR3HBn+KolR3180nn4PJGgwtSurElPs182e8igpvoFYzdWA1B87JlZicWl/XbW1vrsA1Shih+ve/HBpZo0M1DpZ8+fPnOWV9f1WFRYCMC9uq6X7eRwNh18lejdjMMwAggxsHTike2Ojdftci+LxOLSE1AzYY79tojkyp98QNb0mbNadF2PoON+5Om6vqjLe997+BYgFB1npdViYn5WpqMmPa0GlSDnFuAplFPXucBhNelpu3Tqzc/KHGyEhf89MECVokKIP+i6vrp7ut/z9JhQzinM2IRK6bgGlVT8D6iasi6UAJiaU5hxW19OldmFfApQPu5QJOLU/KzMYwB0XV+HSun4N9TM7AlgcnhVxeERa5ZfvWjCYa4VI5JBygDwWzsG+Rfz/VErFskoXyvNUbERSPl/nTVKLC6NRwnkFGDt+R+8lB/Z1tyugrti+sxZ5dbfd6OE9hKUKcjm5/nKISVjN1Y3ABQfd+ZBlq7/jPyszKRd7EdicWkKyqfFPW7lYvP4b4sBbp0+c1Z7esybUbHnq1FZpmx2zqcA0uk+CqWpa6+DTE16mq8mPe2RmvS062rS0x6vSU97tyY97WdjiiXk+QaPiEJoIOU7qHTBvYaenCmTU5ixAJVq8DxUvtL/AWflFGYcllOYYXstdh2vAI118QNZMXycAF7Pz8ocD6Drer2u63/Sdf1cXddviC7/9ktnW/PTzRHRo+YcN1XlRxbi7pr0tF6dn7c70XXdGxXwlU1e9mP7pputrERbSCwu9aA8eo8C6gZtWnv+2FVL/mZ9/Pj0mbP+Zx1rLCqBAsAfbTXpbvM1wLErfuqHlG2G05W8ZvDIH1FFGM/Z2U6JxaXjUPmt+w3cXNM6dc5rmkB+iHK2Q9f1/nTYj++wzQg/i8omKMThptO1xNp2yM6b75r8rMwjgnEDfmeprVsQIqe3+Vb0qFAGyCnM8OcUZryTU5hxTU5hRlZOYcYHPd2H3k5NeloTatDD14ed1IJSVz+xk+Z/knDBxyeeIwNhHg2lbsrvmZ72YoT4cnjdBkavWwlCuID3EotL70wsLr08sbh0KMp7+hiUU8tJv3vjqd+hZl8rUCFT7dyH0iZ9pOv6zyZdsNnCamCDJxR0RgR8XwLMP/zk9iIp53e2Q2JxaSxqhpwY1dK46aL3notwhYJ1KK1F+4t/Oiqs50eUV7bNLrDyr/8AEIwd0O5wd+wvOVZ+VqYwna5H/e3hT0L8qTemMO1xoWzTY/wTMNYOHhH50/g0Azg1PyvzvK0b5GdlXgvcu2h8GpUjUwTKaeXymvS0Pp2qsYsoEcApP81n8IY1XlSd6vtRwrgauAQVh3nRbYV/jkEV+wC4dvrMWa2wpSpXFiprV6cqcJvOsWZP3wActK6qCmDVsLGTmyJjJHBKflbmNg5ficWlUcDbQIojFNz829efHBjhawXl2LXeOmY8HVoLva9U3uoCPgUIxcS3l+Gd/AuPc3pgwJDjcTjBNMtRhVd6HbZQ7qXUpKetQgkB5hyf6TOFAHglPyvzH/lZmQPzszIvAp5qiI7n4xPPbVeJ6rbausv4HEBze8ia9ULsgM0b/ooqj9lOCLj0tsI/f4mKdRXA89Nnzvp0qza6tZyp6/qP2OwpXwCkrVk6GFVTmsLf3i4qxhwM8F5+Vub5icWl7sTi0kuAcuBkpNl66dv/CotqawYonD5z1ptbHe86IAoVktbns3btAZ8AmGHhKVK9hw7Pz8qM3pMD5GdlOkMRUU8E4waoDZp2TW/N824L5d7N/UB9IMwTWTH2kG9QThbTUalNZwacbjHznCtqDafLjUrr+PddHMtmD7DUapUIgQjzcPlr/4ysSU87DpVz+VhgZE162msoU8F4VGja9K32PwzljWqi0jja7DkfAzikPNlphP6C0jjw3ikX8c2hx2sLDjnuVWEay1HhmElCmmsufvffGxNr10WhUs3e1H4gXdc9dKQ2fbC32TG7mc+AdQgxIBg7YAPgQIWT7TYSrvMnJI1RlZ+MV3Vd/6JberofYAvlXowVzvQ/gNkZF46uGjrmVpRADjeF4D/nX7O2MTp+ACpU7RJbbd3lzAUIRUQDXJmflRlRk54WrElPm1+TnrY2PyvzClQUAsDl02fOqt9q37us5au6rtt5338ZP6JCLiOv+mLWapRt/l8Ijc+OPZO5x53llppjGED/ug0Lrn/+b43D1q8aiZpV/2r6zFlbRx9cgkrcUo31TNnsHpZzYgFAsF9Ce4bGX+/u/vlZmYmh2P4PKOcu04/m6BVJQnaGLZR7PzqwGCEGvHb25UcBE4Ep/774psdr+ycORRVhn1qTntZr4vz2I+YAGFGxAVQ8+JaZcH5W5kmo+EyAe6bPnLWl5rWu6wejnJEklgnCZs+xZrPtxVPOq0lPM1CDoLcAktatNE+c/yE3PHcfV/zvn0d6Ar6DgM3AmdNnztqw1XEEHbPkJ3pLkooe5g0A0+1JkkIDODM/KzNyd3Y0Hc5/+hOSItSa+Iuu6zXd1cn9Aee+7oBN91KTnlaTWFx6GSoV4EUPZd/nR4322x1WcmrS0w6YAuAHGEUAZli423S60ELBe/KzMg9G1bL+LUqV/RY7qqfvtJZv6rpuFwDZO94CrgLO03X9+hpdN6wc4zGXvPvv04EZqKxSS1Ehao9Mnzlr+wHqsagMVD7g2R7ree9iKcqcMyYUHb/B1bh5EHAm8PqudsrPyjwkkDjiQul0gWlWoWmP7Kp9b8CeKfcBatLTSoE/Wau/RYXcCOCJmvS0F/dVv3o7VjjINwCB/okfoGa+F6GERBjKWeiy6TNnmVvtMwHlcQ32LLkrKEJFFQwBRgLUpKeZNelpDdNnzpqJsudPAVKmz5x1cycCGTpMDK/our65k89tfgZLazETwD9gcLtj6WU/t5/hiXxwK+euPpHO1BbKfYc81Mj0VVRJtWw6VHI23cdbAMF+gyRwOCoJxTuoPNbnT585a/vkE3ehnst3dV3/oSc72hvRdd2HFSeLigvfhukzZ62ePnNW0dYDo+3270+H/fOpztrY7DZPASEZ5hkWiowFyNxVOc38rMxDAv0HnYEQEAoV67pe0mM93YfY6us+glWP9EPrv03PMddapk2fOauUjnjkHdB1/SDgUmvV9rjuOr5GZU47lj1Pyfg7lFbjB13XbTPPXqDrerWu6/8Ebm4bOioYubLcpQX9v8Mq7bs9ociYJ0Mx/UBKcDpv6tHO7kPsmbKNTffSbhMeouv6TgtTWNyAMiu8pev6993brT5Fe/jMGZbT1m5htb3GWn26y3vVN7kLKMXhdLWOTMZ0h12Xn5Xp2L7RPy45d6p/4NDjAYQRelPX9YU93tN9hC2UbWy6EV3XG1FhaLCL2sq6rkfTYWN7vLv71cf4APCj8u4fugf7nYCqAd9CLyt6sK+wyjmehZQ/SaeLtiGjRkrNceXWbf7+u4sG+AclvWKGR4Jp+qXTtVuVvXoLtlC2sel+2kstpu6izWWobFFLUMkWbLoIXdebgNnW6p74UVxrLV+1Blc2XYCu6+sRYiqm2WaGR9E6Ivmpe3Jv13Vdz/nrn/90c9vwceuCcQOjAYQRuspymOwz2ELZxqb7+c5antTZh5aatN3Dt9DOFtUttGerm6br+oifa2w5eF1ordqq6y5G1T+WlyGlND3hmumJ+AvwhHS6HpZOlwvDkFpr811/uf9vL+/rvvY0tlC2sel+2qs7naLr+g72M+BEVDm7VlTcrE0Xo+v61ygNhAZcvBu7XInl4IWqnGbTxej33PuWq2HTJHft+s2OZi9aazMiGMDZUBuKWLP0uLv//o/79nUf9wW2ULax6X4WAA2orF6dzZbbM329pOt6fSef23QN/7GW03Rd3+m7T9d1J6r4BKgMXrbmopv402NP/hC2ae2oiDXLroxcVfH3qOULnw5fX3Xo7S/MnL+v+7avsEOibGy6GV3XQ7quv4qKDb8BK9OX9dmhqJhlCTy8b3rYZ3gNFX6TAkxDVefqjF8DI4BabAevbmf6zFlNqJKmNtgzZRubnuKf1vI8XdcnwRZbcrut8zVd15fuk571EXRdbwAetFaftgZE27dx0lEM5HFd17dP7mJj063YQtnGpgewclj/11rNs5bXA6eh0kDe2dl+Nl3OQ6iSji7U9d+eaaiZ9GY6BlI2Nj2GkNI2l9jY9AS6ro9EJeZ3oVSp51l/36Tr+mP7rmd9C13XT0Q5fTUDg3Vdb7a2u1AhaaOA6bqu2+YEmx7Hninb2PQQuq5X0ZEY5NcogfwOdrKQnqYEWI6KC79gq+03oQTyJuwwKJt9hD1TtrHpQSyv3yuBXwEL1Sa9ddd72XQ1uq7fBdxjrf4TGA6cg0pzeoWu6ztzArOx6VZsoWxjY9Pn0HV9MLAIFaa2Nc8BV9lhUDb7Clso29jY9El0XU8HdFTRkCrgK+ALWyDb7EtsoWxjY2NjY7OfYDt62djY2NjY7CfYQtnGxsbGxmY/wRbKNjY2NjY2+wm2ULaxsbGxsdlPsIWyjY2NjY3NfoItlG1sbGxsbPYTbKFsY2NjY2Ozn2ALZRsbGxsbm/0EWyjb2NjY2NjsJ9hC2cbGxsbGZj/BFso2NjY2Njb7CbZQtrGxsbGx2U+whbKNjY2Njc1+gi2UbWxsbGxs9hNsoWxjY2NjY7OfYAtlGxsbGxub/QRbKNvY2NjY2Own2ELZxsbGxsZmP8EWyjY2NjY2NvsJtlC2sbGxsbHZT7CFso2NjY2NzX6CLZRtbGxsbGz2E2yhbGNjY2Njs59gC2UbGxsbG5v9BFso29jY2NjY7CfYQtnGxsbGxmY/wRbKNjY2NjY2+wm2ULaxsbGxsdlPcO7rDtjY2NjY9C10XdeAm4DDgO+BZ3Vdb9qnndpPEFLKfd0HGxsbG5s+hK7recD/bbWpFXgZ+KOu675906v9A1so29jsQ6pzSwSQClQl5U1u3Nf9sbHpbnRdjwPWAeHAa8AxwDDr46+Bs3Rdr9s3vdv32DZlG5t9RHVuSTzwKfAjsLI6t+RqS0jb2PRmLkUJ5J+ALGCcta0eOBr4l67rffY5sG3KfZiC7CIBuIHfAecAq4AGoBRYD3yTU5gR3Ff96wP8F8iw/u4HPAOcX51bkpWUN9m2r9n0Oixb8g3W6r90XZeAH3hF1/UlwHzgAuAEoGTf9HLfYs+U+ygF2UWTgCWADyUMMoEc4E8oldIXwNcF2UVD9lknezHVuSXpwBlAEDgSuAVoA84EPqnOLYnZh92zsekucoFkoBF4fusPdF3/HnjBWr29Z7u1/9BzQlmPFeix49FjR/fYd9rsQEF2UXxBdtFU4BOU2giUMHgOeBIljFcDAZRn5Ce2YO4W/mQtn0nKm/xtUt7kR4ATgTqUCu+f+6xnNjbdgK7rScA91uodO/G2/gdgApm6rh/TY53bj+gZoazHDgN+QM3MKtFjn0GPdfTId9tsoSC76C9ALTALiEepisYB/XIKM67MKczIySnMmJxTmDECSEE5Y0wEiguyixL2Vb97G9W5Jb8GpgAh4O/t25PyJn+LMiOYwO+qc0su3zc9tLHpFq4GHMBnuq4/2VkDXdeXAjOs1bt7qmP7E90vlPVYAfwHOHSrrVcDs9Bj07r9+20AKMguygR01D33AW8DZ+YUZizPKczYIQQhpzBjBcqusxYYD1QVZBed32Md7qVU55YkoLQSAI8l5U1evfXnSXmTvwT+aq0+UZ1bMrIHu2dj0520vz/+9TPt7kcNTM/Udf2w7u3S/kdPzJSPBCajVKTjgYsBA2VP+xY99tc90Ic+jeXQdZe1+mhOYUZ4TmHG+TmFGQ272i+nMGMlyuliJcpb8n8F2UWTu7WzvZ9cIAr4jm3jNLfmPuBzIALI76F+2dh0G7qu90eF/oEyne2qbSUw01qd3p392h/pCaH8O2v5Jrp3Gbp3JurmzEKpMl5Cj03d6d42XcEE4CiUnfiBPdkxpzDjG9Rg6g2Ut/7H9oz5l1GdWzIAuNZa/XNS3mSjs3ZJeZNN4DrUbOGC6twSeyDUDeRnZcbkZ2Wem5+VeUJ+VubQfd2fXs4J1rJc1/WNu9G+fTCapet6n7o33SuUld24fSb8csd2bzlwHvABEAY8b9uYu5VTreUXOYUZu/NAbENOYUYIuBz4CPCgZszHd2H/+grXoma/36Ou5U5Jypu8CHjWWn2mOrfE3c1961PkZ2WOBipQZpwSoDo/K/Od/KzMuH3Zr17MVGtZvDuNdV3/DqUtcqKiQvoM3T1TngwkoILC52zzie41gCsALzAJuKqb+9KXOcNafvxLD5BTmNGEerDaZ8z/KcguiuqCvvUJqnNLHMA11uqjSXmTZUF2kSjILkotyC5KK8guiuhkt1xgIyqE5Lqe6mtvJz8r81jUC38wKr3jSuujc4B38rMy7QFQF6LrugM1CQM1CNpdHrWW1+q63tnz0SvpbqF8mrV8D927YxIK3VtDh63zXvTY6G7uT5+jILtoIir2FeC9vTlWTmFG+0BqFTCCDockm5/nRGA4aoD6mqVpKAUWoiITmgqyi54pyC7ytO+QlDe5no7Qqburc0vierTHvRBrhvwhMBRoAQ6dPnPWaOAIVOzsiXSE7dh0DUcAA1GJiebuwX7vAitQiXV+2+W92k/pbqF8pLX8ahdtCoFlqJvW54z6PcDtgADeyinMWLy3B8spzGgE/mCt3liQXXTw3h6zj3ChtXzznYbg2aiX0yGosKgg6lm8GphZkF20tSnneWARKoTtZTupyF5zJxCDGgwdOn3mrOUA02fO+g74vdXmtvyszCM7393mF9BuT/5c1/XdzhCo67oBPG6t3mxlA+v1dN9JqlCoI6y1BTtv5w3SMRuYjh47sNv61McoyC4agPJ2h63iYfeWnMKMD1CjWCfwZkF2ka3h2AXVuSUaVjhIld9cjkqv6URlTktA2enPQoWqncNWzniWM9h11mdTge+qc0uGYbPH5GdlJgCXWKvXT585q3Lrz6fPnPUW6t5owFP5WZm2n0vX0C6Uv/gF+z6HMnFOQGUd7PV058hjNBCHymv608+0fR0VIhKFGsnadA0XohzpfkBVX+lKrgbWoJKPPNbFx+5tTAIGSymbytqMq1AC+RXgkpzCjPqcwgzTGui0Jwu5rSC76DftOyflTf4clWxkNTAW+NCeMe8Z+VmZAngV5Wj3IzsXELeg1NiTUKYam73AKizR7hT65S/Yvwl42lq9oy/MlrvzBA+xlovQvYFdttS9ErjDWrsOPXZEN/arL/Era/lqTmFGl9botLy4LwMkcHlBdtG5XXn8XsbZAC0m5SaMQWVVu8ay0W8hpzDjVVSMMkBhQXbRmPbPkvImf4WacbRnWSu0K0rtEacD6ah8CRdPnzmr0+dh+sxZG4C/WKt/s72x95oJKNOkDzXx+iU8hnLIO4aOYha9lu4Uyu22xrLdbP8pyl3eje1osddYntEnW6tvVueWaNW5JUdX55ZMq84tSemK78gpzCgBHrFWXy3ILjquK47bCzkGYFXA7G+tP55TmNG8k7Y6yjM4Eni+ILtoyzOalDd5DSrE0ECpYS/trg73QtrDagqnz5xV8TNtC1DhUgPoo6keu5ATreV8Xdf9v+QAuq6vA26zVh/QdX3Mrtof6PSEUP451bVCzZbbMxz91k7BudccjVKTrj43znU6UIPKdf0CsHhN7ucvV+eWxHbB99wBzEbZRd8uyC5K6oJj9hqs2exhALUhOQYlUJ/trG3qjNRBhcf+8flX0u4bEdICJjC53rNhG6FgzZjbvd6frs4tOar7et87yM/KjAROsVaf21VbgOkzZwWBm6zVG/KzMpO7qWt9gXZ78t6WYSxETdrC6eX1lvcfoQygexeg7D4CeMRyFrP5ZUwG8Ai+QTl5DTQxA8s8qwMmJgJxmURuqs4t+a46t+TZ6tySI3Z9uM7JKcwIoAqV/4hSU/2vILvI1VUn0QsYCgyQUspGQwK8l1OYsX77RqkzUgeibG6/9YZvGjFvxDsaQFQg7i/nPHzZjds1fwCVqjASeLc6t8QeCO2aKahBYxXKk/1nmT5z1keoEEInUJ6flflUflbm+flZmb3eptnFtGvP9tievDW6rpsoP5Y2lBniyr3s135L9/zA9NhIVMIDUKEHe0Iuyv5wMh3ZwGz2nOMBDolwhAERIYzq8ybc1HbjqDz39JH/oNq9AYFwAYejfuDzqnNLrrdU3Eftib0ypzCjBZUj2wscS4dd1MaKQGgyMU21/u/tG6TOSBXASyh780rgvPKEeRduilzjdZlhTKo+/bEjnz3mj+3tk/Imh1DXeyEwCHijOrckrLtP5ADmJGv50c5syTvhZpSjKkA28CbwcX5W5oCu7FxvRdf1BNRvGrZyNM3PykzIz8q8Pj8r85L8rMzddli0cmK357X4h1UKstfRXaO+Sdax16J71+3Rnrp3FR3hO4+gx9pepnuIZYc8yi0g0SkyAF5IeGd1UAvFAvMrwquOyR59b+MfRt3Po4kv+5d4qjahZgT/RKm4vwY+rc4t2W2HO6uqVPvo9faC7KIzdtW+DzEZoC5kOlBOWh920uYWlCOSDzinbFrZOz9eXvpGpD/ukIDmaxvQmsTRqzMfTZ2R+kHqjNQ/pc5IHZiUN7kZlSWpHpXX/JFOjmujaPf+3aOQHCtkahqqyt0TqFnaFODL/KzMXikQupj2esjluq43AORnZV6JSprzT1T4WVV+VuaT+VmZu1uz/VHU+ymWXqrG7i6h3G7n+qVhOHlAJTAEeLBLetS3mADEjA/TQkKIyI3Oug1v9pvTrka6oWxa2deGMM+q8qxd9FH8V2E3j3xo4IyB77YGCa0FNlvtMlAxsX+0Cin8LDmFGW8A7XVSX7Lty4A1S6sNSYAZVh7xLaTOSL0EVdgd4E9l08q2mHtue+Lc1U7TfbZEypSNx3FM1blnaKZ2H7AkdUbqr5LyJq9EOXtJ4A/VuSW/w2YbrJnY4dbqHqtQp8+cNXP6zFm/mT5z1g2oZEirUQVa5uRnZQ7qup72SjKsZQlAflbm7YvGpT3733OvHjw741f+pSNT1oc0RzwqGVGxlW1tl1gJRS5HaTDOQGkwehVCyi6NlFHosa+i7Ix3oHvzOmtSkF0UhnphxQDrgW9yCjOCWx0jHSiy1s5A9+4ygb9NBwXZRb93wvNnxDpNhxDaXcOe4NuoxQB3lU0r26JaTp2R6gJ+g6pfOhiVBm/5MH9i1OMr/8/wyLCDrKbNwFVJeZNn8jNYaSK/Qjk3zQNOtuzOfY7q3JJYKWWdEEL7yBvEJznaqroFQOqM1GEoG2c0qirObWXTynZ4IAuyi3KxEopsiKpqnp1SGBVwtoHy1L73g/In70aF8fiA45PyJn/f3ed2oJCflfkwSg29BEjZQ/V1Z8cbAXyGSjP7I3Dy9JmzGva2n70RXdcXAynAr6PLv5ULDjn+9bnHnblNG2cwUH/m3DdJrvwpHjUhuAuVVKc/0DZ95qzV2x/XOvZNKO2QDzhK1/XdjfLZ7+mumXK7k9eP239QkF3kKMguugjlAPYR6gZ8AawqyC66dksIiO4tRqmMAGagxw7upr72RiYNcQscQmgbnXXN30YuBnhxa4EMUDatLFg2rex5IA0VAhIHHLEmrCb5ovG3DVkUvvwfKLtlFPCf6tySny3ZmFOY4UP5AjSg7MsFVj3nvkiGEEJrNiQ+SS3wbfsHqTNShQP5NEogzwdyOxPIADmFGXmomPOmQc0jo7JKc2sjAjGghPIrN4zMe5AOD/h3qnNL7GcFsGKM22dSN+2tQAaYPnPWKpQn9wbgUGCW5d1tsxW6rieiBLIMq1m9qCEm/pmSo9od4PkO9ZvfFHK549879eL4N874Td3yEcn9TSGerI/pt+mHiUdVLEyetOrOa39fkZ+V+Ssr+cvWPI4yBXmAN3Rd74pIkv2CrhfKeqwbpT6F7TwdrdjZYlQB67GoJApfWsvBKLf3Twqyi9pfKrej4pwHAa+hx9rOLLvHYUNd6ta+H18Shfo579T5qmxa2UaUau56lKPd0qAWir915MN/OHfCH+8GZqBqX8+szi352VR3OYUZlSi1qomq/nX7Xp3NgctpABtDJsDbOYUZ5pyiMVPnFI155b4hrcv/NrTtzBOjggZwZdm0stCuDpRTmPEmKrxkfWQwbsDFpXduivH1DwIXLQ9f/eHLA2ZdhxpYJQGz7YxfgEoxG456D3WZps3Kl30aauB5PPB2flamZ5c79T1UNIeU5e76jY9/nXZiP8PpAmnOBY6sSU87FhgGPAywYmRyv7fO/A2PXnm3fPbSW/j0xHP46OTzeS7rxgnvnpr1em3cwPctLQWwxRv7d3RkFXxV13VnD59jt9AdM+VxKKehJtQFA6AguygVeAfl+NKMcuZKzinMOAEVNnIzKmtLBvBDQXbRiejeNtQMwYv68T+PHmuHJOyCguwizQGH9ncqSTwv+keAD8umlS3b1X5l08qay6aVFZRNK3sQZYP7GIgMaMG3z06+wRUQwTcBF8rT91e7OhZsyY99i7WaV5Bd1KdKc1bnljiklOcDbAhKgKfmFI05BRVmc3GUg9FhGlwQH3Q8Oqx1t5Iq5BRmLEQJ5hVuI3xgVumf2sbUHtYKnPifge+//2783KtQpR4PA96rzi3p6zO49ixzM7pilrw102fOWojKV96Cmjm/k5+V2WfKC+4GkwBE0L+uxRN5yqLxaWqr0P5Sk54mAWrS0/w16WnTURq1x4FGw+kSSCmFac7VDONLhMaSMam8fEH2GT+NP6wqPyvzp/yszOvyszLDdF3fhMop34ayLz/TG9JwdscJtNshF1sJQSjILroFpQbNkMi2n9yh7Ifi2v6cU5ixuTw5pX/G3JyRGXNznkUJg/aZcVFBdtH0gpq3lgMXoarpXAI8ZQvmXTKmn1NEOYRgs7PBWO2ugU7CcHZF2bSyFlRqyMcAGRLGpReOnx4mka+jMq69Xp1b8mer0MJOySnMeAx4yFp9piC76No9PpsDl3QhxKCAKakNyS+TL7r6B1R2KBGUrK3wbXPp7ur8EDtieblPBr5zSEfMqct+H5G+7DetTsN90FOJ/3vttX4fT6ejBOH7XZQg5oDDqoncnk2qM4/3vWb6zFnzUEVCWlEz54/yszL7dcd3HYBMAnB564b9kHoMhtMFqjDRDklEatLT5tekp/0RpWE9DyFGrp9yePq6UyadABzqDvh+DLrD+CDjV3x6/NSDDE0rAH7Kz8o8Q9f171AaERPlAPbkgS6Yu6Pz7Z7XPxRkF3kKsotuQDmxUO0wAi9G+cM/CA+8nLniy+qFEw9eg1JdLwEaMubmPHPcV396HGm8glKX/gN4u6Dmre9RqgqJKhT/MnqsrS7qnIkJ1ix5QeQiB4JN/II6ymXTygJl08puQjnjBYNaaOr5E27+ABXKAHAv8NZueGb/n7WPQOVzvnfr1JG9lZCU5wGsC0pMYTyA8o+YDBj/qPF8X7jJw9ObwtpVqr+bUzRmtyvg5BRmrEPNLv4GyAm1R0Zc9GNuYGDz8MHPDXr72ScHzXxCItsFc0l1bsnILjy1A4VjUMUnNrKLBEbVuSUR1bklR/7SWtXTZ876DBXO5kVpMebnZ2VO/CXH6i3ouu7ACkMz/a3jfzjo6PaP8tpnyZ1Rk55WU5Oe9k5NetrqrbYtDLg9k7Dykf+Qeiz/PffqYGNU7Fjgg/yszFeiy7/9GlV2UwLXolTZB6zWouu9r/XYr4BjNwTGXf9a3YN3CcQggAVhQeaGhwDkJRWfmL+r+GjrsmhNKIcXACSsLZ/wmy9rEo85DyHcqBSRf8hJPD8ceBGlHv8GuBjdu7JrT+DApiC7KDcj2vlAtENw/9B/8UXMD38tm1am780xU2ek3ony0G4D0j8ofzIVlR/YDWwC3kc5g41HqfN+QDkefZKUNzlgOXrdS0eJzveBy62iFr2Spbd9viTCIcYvbDXWu8+96l4h5JOAWRcSf7lnffhfAc2/8fTLnzjsneMdmnkVShN0xZSMypdG5s4WKJNOPGoWFgTWop6RSGBjVd7UIEBBdlE6KvHIUBNT/jikSHyX9CEH+0Z/fP/qGw7V0AYBdcAVSXmT3+np67CvyM/KvAM1aHlt+sxZF23/eXVuibsF/99DwshuEC1hAUJtwLT0v1302i/8voNRv/nhqGfgRuD5rlabHwjoun40MB/TDCx1ud1FJ2QCLAcm1KSnmQBzisa4AaZkVO52ZEZicem5qPd/jDMU9J/6+Tvug5aWCqFs+39qSj68DqG9iDKz/Qhk6bq+pEtPrgfoWqGsHLEaAffLNf981EvSTQCfeQL0q/ly9WUVH9fG+5qiHMjxAC8ln8ZbY05c0+byXPzB27euRVUdykHFJ9MYPXzTj6nXGUF3dKL1DbMnRz/zxiGRH+SjXlhNqNzLT6N7d+ko01d4OafojZOjXRcYmGSNv40WR9v4pvK8DahSmj9W5U3d4xueOiPVgaqffBbqBX/KB+VPCpQD2MG72LUWlWv7maS8ycsKsoumoZz5PNZntwEv5hRmmHvap/2Z6twSEZLS7xTCtcDf+GTM2Tf+CmWSufWPqyPChOD+UOtI2lZl49KC3919zN8DQ6I2HAtQ74tpnLP6JMdHqzIiTblNOV8JCJDEhzWE+ofXl69vSVjQEoyqGBbUFma1uC8XiCyAprA6vh7+Hk3Rq+ofXnVrYz9jS9W1/wC3J+VN3rOEPgcg+VmZ76Hq7948feasR2FLHvLjQhinf+paeH21VhfPdj69Humq9hF8FMHLuq5v2MPvTECV5GyPz/0E+OP0mbPK9+5sDix0Xb8b+KvZ2ixfPulc0RoRDXBdTXraU3OKxgxEmdPOQIUzZU/JqPzv7h47sbh0NOoaHwUweMPqptM+fzc6YXMNwE/+gUNeDvQfPB0hBqImEX8FHv2lxTD2BV0tlI8G5gdCrtoX1j4dHwyLdzjWF3NE5azayJBvGzVnoyvihayp9xyHml2B8sp+9LZv//tpRvX3v0PVVR5maE5Wjsz0rR42xY3QNMB0i+ZZU+MfGD7EvTjN2ncxqrLUG31dOH9+89x1o8Mcg8td1dw85oHK5ooHpqF+xMNQoWenVuVN9e3pcVNnpEahXjLHoAZDv/+g/MlZwHSUSeE91EwhFqU2/RWQuNUh5gD/LmoMLmsy+TcdpT1LUbPwt7YvZXigsur/Ph/hEKLKlJKFhz70WHji4j8Cq5+piTl0sY81id5gVF39OWwOqERTApMLx7/LKcPn4tTU+KTBF8O6lsTAsvox4uNVJ+Mzwl2HDVzI+eNmMTSqBoCWYAQfV6VTvOYEWkKRdWl+R+nJba5DXIgBAJvD1/HTkLlkhEbXT/WeECcQAvWiegJ4tLcKZ8uevA7oH6ZFHH/eiBuGoOz2EyQy7EvnEiqcawEQUvgk0hsjw8OahC9OCut9KAkheB81qHx/d1/q+VmZDtQzcQ+qlrmB0mQ8NH3mrMVdeZ77K7quzzfh6A8mHM6axOGgEkFN/I/8lQHMpaNIRTszgbeBt6ZkVP7sdU4sLnWiBvR3Ax6klGOrykPH/PCZa/DGtZgud1nriGQhXe72CcNK1Dvm5QNBOHe1UL4hYIY9/saau0J1YQc53f4Gjv36rzjMLRqKJ1E3ZX5KRfmakbmz+6O8sH+HUkmDephejfM1vvfiR/dPcEnjVmBsS3gClaPPpXZg2pavi9DqVx8a8e6AsZ6vImKcGwGqUeqNmUBZu6NZX+HDP871jA3TWj2aEI8M/B/vgRnYdMb29tsXgKur8qbu8eAldUZqLOrhOdna9BZKLb0IlYv5NFRSBc0pHbXn1qWTWX/i2EHB/odaAgGgWUo5a5nfNJb7zbODkvbQnTWoe/casLCr6z/3JMtv//xJjyb+UGf62Xh6dqUQcgxBbil7ZtD1R6yuH92/CUyEbHaFf/DXYy7fvLj/qCOAlLHBVZtOGTF35eEpP0x0OMyo9uOJNppkm+aln5kEICWGIR1Bp2Z4AAKGk+82pPF1zSSW147nMF944CifU3MhnAA+Zwst8cs4iSRjTHCwA0AiQwLxHirV4YdW2s5eQX5Wpg78JTF8VMOJg35dL4QYBWAiWeBcHipzrnYikQNlzA0599xSAGoWXS9a7liv1evLHOtdm7TGLcdzSM17cvCgb0aZCYkoM00jsAr1uy9FxZ+vTMqbLLfqw1iUT8zWdcY/R2kr3rHqNvc6dF0fiJQbVvVPFB+kHouQpl8KLb0mPW3enKIx1wBPo65fJsqZtL0kIyHT4d3c1m/RxrYBuTddNOtnq0olFpeORGV/zGrfNrB2vXnQ0lJt7MrFRDoczf6EJA2Hs92+XAu8jJIP31hhVfsdXS2UXypac+Vvyl2ZIE0GrXxr3cTVRY8LZRNblVJR/kZnu43MnT0cFSN7BSqTSzteh2l8fuGy4oazV36V3M/XOKk5api2etgUNg48DKl1hKVFs94cGvaTNjhsKQNcK4lzrF3r1nxFqOxSPwCL0L295sXTGd/c8tktQ9xavs80+dW4O/GuvBnMcIDnUbPc/6AcruYBt1TlTZ2/p99hZQG7F/UwbRH4mikZtgkS6yXhAaVrbQuDpnCBw9OPI43jQ1OajzP6h+K2xJpLpNlmsnFj0IyrM6Sn0ZA0G2Ao+2kxKsHAd8CinMKMpl98YXqQ6tyS/qaUNZoQzp/GP1vvGvlFvGgjMPBOl8PpFw4AQyAdcovitFVCsUQcpyHjAUyPJDhMEhosaZ5iYAzsOH7kHI3o2Q6EH3/bUWaw+VQzLDREbqnKFQo6WLdxCJUNY/B7JzKobgThrfFgfV0/d5DRESZD2SLzMZGhkAgtcElnkUAsQNnjViflTd4vX1q7Ij8rsz+wanhkSuQxA882hRCaRNbOdy77frGj+lAp5CCAdRHrnpw3aN7XKAetjcCPZdPKWi2nuPvqRfMlSx3rteWOGtpEAE0KDg+N5mBjGE4cnX11Heo98wMqgqQcWDpz5YPJKGfHc9nWsbYUJaS/Rl3vZdNnzjrgM9/puv57d1jz8x8efgRL3cmcJt9vywg894eVfsfbJ0aHlgMD1gS0h/I3eO4vm1bmfeC/F56tIe8fHVd1cD9PwxZjQqM/ytcSilgppfg2Nqzxs0hX20KUutuFSrHpBTZPyahsSywuTQVuRXlhu9uPEeetZdi6VSS0eIkzDBkX8AmXaSnjpNwMzEGIeah3zE+6rtf3zFXaNV0nlPVY98pVZzetjLnCvcwP4Ws/KT1y2duTUirKd3iwU2ekXuAx3f/n0wJhKIHtQaLFhqJldGCgJyzYP0aGYuKk6XGZhpuQ6caQLiJaQxy+en3w8LWrjKTGlrC6fgeJjf0PxhszGkPs+KCEG3VEmpuIoI4IUU+YaGl1CV+9U/hrHVpwo0OENmoitNGhhTY4tGCtwyFrnc5AndvpawjTmpujtM0tLs3vt/oYBEL76+y7OrdEbDIDtQM1d78f2Mj0gXNfCGw643VgbVXe1FKAkbmzL0DNlKNRarXrgad/oZ354AifvHvsOnn+oSulM32hlFG+7S1029LmhrbBo3ENPoyY+FSiwjrPQd9mSlpNic8En5QETAhIoyWEWWsIc4MBNQayRkqxEaltEFLbJKXmDUnZEIKmgKQ5JGkD2npSmFfnloi1oYb3hzrjzqgTTWzIuBHNIen3hBPPYo1mD7x4Qj/vhfNbByU0+yaiohLStzqEiYrhd1nLcjNMehvPMxIDY+TYqCLNiJjvMIEBWC94iSQ4UtJ6tInvUBMzbsd+mX4HwaZ+BHyDCPn7YfhicAaiiTejiJcRRMpwtJAHYbrRDBfCdBEyhWyWRlsrRmObNOv8QtYGRGijKcwNEnODJuQGDTYizDoPjoZw6W6MN6Ka442YLc9LUt7kHjdHPH3Zb/LT+mXcMjRyHBJJmWPVvK/cFZFO6ToEwO1pDC2PWCMXxFS4tvu1rgd+XTat7EuAFbd/PlbCTQjjws/diwescmxyAAgpGmJlxJfDzQE1I4wBUf1l9FgnjlS2EgbbUQesCpqBTQ2BDXH1/g3DWkLewQHTR8D0ETIDhMwghgwaJuZaU5prpDTXmhjrTGlsMGSo1pRGnUR6Ub+JFpQJwo8SUgE63k/GvnYs0/9+y5K5h08aX+E4CJcM8HduIoEOpUCTgaGvC3eEpLambdXVHxtto38PODRhMLH/kvWnjyiKSu63LFoTu3cahqn5QtLRYJiO2noZX1/sOC3yB23SoA1i8BAptB3eR1H+ZqL9LUQH2ggP+AkPBAgLBHGFgriDQb8zGGxwGKFNDjO0yWGENjpMc4PDMDc6TKPWaYQ2ucxQnZA0omlN0uFslpqjDYdji3zoitn3HgvlW597IDR7xFGdDhUdmEzwhrjnaw+b3eveeWDY83HVYRvWAc8+uOqmz/4z4P2pDqndfmbDCcdPbjqcCs9KVoWtZ4R/MCP9Q/DIvU/YJaXEBAxnK6GwBowwLyFXE0FXC5s9ks1hgs0uQb3LQZPDSYvDSavmxK858QsnAc1JUDgJ4SQkNAwcGELDxIGJwETDFBoSgURgIgCBBORWA2GJ4Ki6cp779Y1dmmLy/ofuKH/58FM6LbqeFKrht0sDLFtd3XTvQ3d1mtFpZO7sYajY4XaVTxBlMngYKKjKm7rlRVqenCJQKTinoGzAY1HJYQRKqMex7cvICyz2a87WZndEnCFE/zAzGB8R8ke5DMMhHZJQgiSUAEY/iRnvhvgYZHQ4MsKJDHOAU2KKEE2OMDZrUdRrkTSKCJpEOK2ahzY8+EQYfuEmgIsgLkLCSQgHIZyYaBhCw8DJUP8m3jzr6i5P8XnQnE+kuZNowpGhas7ZvJzRtdU4D/4cZ7Wg5r8uXjnJwer+bsNnJh66OPv9Rdb11YBzUDGd3wBzUyrKf3YQUZ6c4kJl7ooAIjSXOdQdHToGQaovkeTWYWKQfwgRwSQpQgn8osDHAC7q6UcDcTQRSzPRtMhIWonEhwe/9BAQYQRwEbLug4Fjy38TBwKTr6Zc2OXX/7QPX5ZrXDvPJDosuIFjNq5ikK+cftHriIyqQwgT09VGlCtESEKriQEiuDbgCDYaRNQawrFo42A5vvrUZSPqD3Z5QpFJVmlTJBKfZ4PZGl0lTUdgh3efNDWp4ZBO6RAuNJwg3M4gLncbvnBJa7hGa5iDNrcTn1P9DzhcBDTrfaM5CQkHIeHEEA5CODCFwLTePbL9vYMAof5Wb22x5T3U8RYXWz4DuPS7ORV/uv2BlC68/Fz++j/l/PjOo77qtQ5FZ1LDs5ypfcLxUSFcAvwmvF7vZkGr0nAaviGIlTmcS/iiI3C+dyiOgjDE2qv6LTgiIbz28kNbl555kLF8eGS/Zi2UIFWQbAgMtwCPpJN52BZaiaCCiSxlAisYyxpG0Ci6JmRfSHU3tC13Re7wH+sunLPqK+Phy3P3KNPYHqclCzodYusLvz218fDJ2GourBx+7sNVt/FCwjsc0jrukrG+YfLB1Tdt84Am+0aR7Bu1zf4hjLaQCPkNYUqJ1CTSAcKhIRyaFJoDh8MhHcJhvWlCriZ8sSvwR6/CH1WNP3ID68IlS13DqWIUaxnGeg6ilgEYwkVP0uqu7PJjmkIL39n1r3f35/aD4eqG+VFNtw4YHv2P2h2SuVflTV0zMnf2JaiC79NRs7IRqEQhd47Mnb0aaD5v+efFv3WGXRIR8v/cA70amFc7ILZo3gmpJ8Q7m08fJjYljBbrGC42QpjBhoQwNsW7aYxxIh1b/wTaqCOcSkayilFUM4z1DGETCfjF3oehO1zdo32tF/0wd/JGqHf354fBh3Jk4jwuoIpV3iBvXNyAND0+KR1TF1/z/pbUs5YW6W3r/26TUlEeRI/dgEplmoWKf95mRCtbwFio0dTqYq2IZnOYh+ZwF4EIQSgCRIRACwczzEF1WBKrXSOpdgxjvTaEjQzCK+J3/OI9FK+a7J6JcrMjgl29g+rD+rNw2ETC5Mkcyg+MYykjWMkEKgBwCohx4ADpiAkPbfmhnRWzRgQS3h8fbP0aabhBhJCuVjDCaF59lNZWNwqfGSLgqYPwzTgi6gjzNCPDA6IuKkZsDO/HJnd/ah0D2CgGsZkBBMXOJtA9g6k5ujw1cZvLtcvr75Z+Hv2unjHfxOI2fsXH/dv4eNIm1gSaafMlE2qaaESOLHCMlw7ucTj9/QzPQaikU7lA8Km6tNUbq2c7Yr6rGIk0MISLqkHQvxHiWgUiYgBazGBE/4HIAdEY8R5krIYRZWC6WzDcTcS4WhnobOF457eYzi8wHX4aHU7WO+LZ5Ihns4ilQcTRRAzNRNFCFG2E4yOcAGH4cRPETagTmSGFhoHG7vy6DYdjjweleyyUh1RtePj6Ta/tEPcX1GT4tymjB34XdgTvjPBy7GofQ4ODuKHmkvYmAiCEYbRpvo+jzcinUTOtZJTDxAKgamTeybv0jptTNEYFpktxDsgzEUwM4eAnDuFbjmYhaWwWAzvdV0iTKLOZKKOFyFArYYbf9BgB02UETZcZNFxGyHCahuE0TcNhmFKTpuE0TOkwTSmklA5TokkpNSkRIDVTIqQpBUghJZrccppSkxJXm/+Hbdw8uoCwppabrv/+tce23+5zOZwfphw5pNo5jAVpR4v3Xir/en1W5tjpM2e1bN/WUlfnjsydfTcq5/ivgD8Dg/q3eQddtLSIc1Z+mQ7g15z8NGC0L6g5PzyqpvwVDbkUNbvWooe1RcQc6zulRYRnT2B91mTREfnhdwlWjIxk/aAw5FZaJMNwGmWBNP/X8hjXIneqa7Nr57lHwkNtRAZbCQ/5pCfUJj0hv+E2AqbbCIScRtBwmUHDYYYMpxkKOaQhHaZpOqQpHdKUmjRNTyBYy6kX/NJLvVOu++HNTivXtLmdrh+GD0/4IfowxwJxLCvlWCa1lJxshhcFNJe3/KdpZQ17/eUqm91VqFzmW//Q64CfTMmKisaBWoU3YegGX9TYVsM1HISQQiMUFUsoOg5vvwQqPSNZ5RlETUx/DEfnAwyHEZIRQZ8RHmwLekL+oCfkC7qNQCjMCIRcRsBwG0HTaYZMlxmSDjMkHaaBEykcpolDSqFJKcmYtNenvD2Z33/9ven8rtMfTsCNZ8mwwQN+jDpYa3DE8Q3H8Q2qaqlmGiZBMxTd2OQ+snYlh2xqZnDUBtxhTUQmVBA+YAVhsesJi12/zTElEDN4ERJoIoZqhlHFaFaQwkrGUCN2XQo4ymwkxmgiMtRChNEmw0M+GWb4pcsImS4jYDpNw3SYhuEwDEOTpukwpaFJTE1KUzPVzARrhiIQppBSCoREqreNMCUghFCTeoSUAkBIcLa23rS313t70n6qfPdgz5q0zj4L80UMTPMdH54SjIQxqqT6BYEWpr77LsbmjWz2zVkSHXjvvM/PPvl3Z4rz7nALT1iTrJe1YS2BEYEhYRqay4VrzNCk8zAHZFAtl7NsQKt0aeGtMYFBMsyfEO7GveMP1qv+S2lgmI2YRiOG9OEjjDaiZZuMEeFojJBKxWRKMDEwRQhTBKTQWgNCNIYQm4JSCwQRfkOIkGESNIIOMxRySNPQpAwKiSkkhpCmKaQw1A3B0NBMgUQipCY1EyElEmdrYI/j3rvMpqzrumiJZONLR5w3QEiTR5rv+uL4ebceL6TTaHA0/VQRvrLxlQEf5D5W9X/fJeVN3mOHhjlFY4ajsrVMQyVWoI5+fMxZzJUZNGkdqgnNNBnQ0sDAxgbiWpqaIv2tiyMD/i/i25q+chvGEmCVruu9zunrxlf+8ur/Es/PipDN/OGZB4kU/n9d98rH12zfrjw5JRG4ARUv3ACIgOYYEnC4josI+idqqADZ4qTDfM8cfI7W4IluH+6XAc+nisqGXOerVx2pVRzjFsYW5egac6C/ifBvRVLj0vWjHRdIbYu+6ItvjKM+f897fvra6OFH+92eLfsIKenX0siApnojttlbG9XWXBHpa/0q1tf2cZQ0SwGvruv7pR2/M3Rd1zbEhre9lXa6G+Di9e8//eild3ZNzVc9NhLlrNc+1FsJPAO880j58cJEuwKVinaLlDA8kQT6DWoJRMd5qhKGOhYNGcXa+IRtDuswjKawUHARyAU+l/trU3MssY5dt6sMTPszrXrimI9DZy18O+68iKqERNYMdNLi2VaP36/JIH1hK6M2hvAEzPKKQSteNgYvHq55zJGbPYmD67WExLURI/rVh8c5I2ULfi1spxqcgYFNjGxb4x8S2Lgh3mxcHB30zY8NtM4f2bb2+8zr39vUE+e8X6DHOuauu748FH7quHjNz6CwbRNrGd41hNb/KF2jThZaWBShTUto+/pJqvq18fERbkY1RHHW+kNwT5iK5tmZujkoXWK1cIo1uLRqnGIdDlFb76DuO0Htgq9bLnWXt0453iejj6Yj6gOU9/U3tDv+wjKs3/n+FO3Rpd7Xuq6/+P7Rh/92tWc4v5HPkekvKRr1xYPXjrj/tOW/9JhzisakoGZxWSirAg2hWN+rzZe5v4o9STMsD+zwgI/Rm9YxYvN6mdhY/5nbCL0GfKDrep/J+KX//Y7DXjrirO9bRDSXfvkMKWVL/VenLPg/VE7xxUBh+atDDgZeRQ0Yd8YXwD0pFeWfjMydHYHKTnQXEHGq9i0Pup6hn1BjmlJzNO8Zx2363hz3tx/kuMf+fdqN5wCvo7QwP/7H/F3BvKbjb6yPG7AlyUiE38fI2nUM3VyzOrFh04uRUs4AKg8k4bsr7rvp+k+/TJs85YcRE9CkIU3huKgmPe31vTqoSszzISoczQ/c8e3moU99tnF0Jur+TN6qtTcY0+87f0JSkulyj1+ekMS3I5LxRnR4XAspv5RCvGkds/xAFb67RI89GHi9pm74hM9qr2J54kHURbupSHJRnuSi1dOhKIxsbfF6AoGGgMsZ1xQZvUvjY1JzDSktlcHhoZrlg/2bSiY2V76RUf/NZ+je/T4GticoyC6KcOLfGCIsMj3maV+M/6wZ0pmYitNzmBBaeHs7w1vd2vD1w69pvuYLnGZHRkcAnJ7rojMfrwAOdonKg8O00pPdWuV4l1iJU6xFCLMNVTTnfaDom6asygUtF5+L0iAdtNWRvkSFbn4ILN6fhO/O6GqhfOOipOGPlYw5nLFyCX/lToA/TMmoLNzTY80pGjMEdYGnYbmqBFodP76z+vyxH446N9LnUSOwwQ21HFJdyYi6miZNyqdQCclXddlJHUDoui4WH9kv+HnEiY7kpp84++VXuMQoQwuANCHQ6DRaN4U5ABwew++JC5b6GlyrzJBokyGxHkR5v+TmTQmHNEYJjXjgaFTGruFB6djQTPjgeNGcAFAtB3ifC5356kvGqY8FcVZU5U2Vc4rGTEIJdM9KRs9+wHdXVEt4zEmgZsQjNq8nZe2K1mGbax7THM5/6rq+fienckDzQPYVZ7cMGflu8YTDqUxIAimDCHFiTXraHoegbUGPfQa4GmgKGI6z/rn0uCOBm1BpHUE53r0XiBtQ5E8ccQ5CnLIpKpbPx6XJTTHxarYgZR1CFAL/qklPq9qLUzxwUIOZXNNAr1sexcbqwRWy1jeoLcwd/8ppZ1M86VjWJSTusFvShvUcUVnGibULOEQsa/YN8qx2OYylSb4NX/QPeYuAhejeXpHspjsoyP70/0DLCxPNTIl9rGKU59vjqn2zkFLehBH4tQw0L0FoVwx/7Jz68uSUBFTd69+gHEnvSako/wt67HCUA2p7VTqJCu18AXgX3duivqtoPCpTYHskQyNKg1RolZE9oOhqoXxss9vz1cvHnoGQJv/kauJp8ALjpmRU7pYKZ07RmDDUy+YuVJ5fpOSdhUUpgTfHXPrr5aOU11+/Zm/o+Moy59CGWolKSqLrul7bZSdzgPLXf/9xUeGoaROl0Lji1Uc569sf6d+yVQIvIYkd2cagw7w43Nvc+82o/MuDduNrHgFy0b1bzBBzisaEAz/4CJvwIldWfi7TR0lN04SUjN+wmsOqlrTGtTX/GU17Stf1Pc4odiCRn5UpmsekthruMM/8ceNYOOQgUIltDq9JT9tzVaYemw08BUhvIOzcZyuPugaVfAFU7vGnQ5ExL7QNH38tcIshhOO7Ecnm98PHC2VqpAl4EHisJj2t15ltdgs9Nh+rlKiUVDdVe55cNy9+qTSFe0O//knlI8dm+MLD+o9k3bBDWZo4NLYWd3SoSgjuAl6xBfCeUZBdFO4gUGrgHu/Azwkxz80++O+v7bLoSnlyihPon3LxulqUee1+VISBicq1cP/WtQ6snPo5qGgSDypELB/4R05hRkN3nFdP0NVCOQIpm988/CSxMaYfl4ZeWD/V8d5gVJWWX0/JqKzY2b5zisYI1IsmHzVaApjXtjns7s+Lj7/3vVOzjqmPG4AwTXlkVXkobc1yl4ZcCVym6/q8LjuJA5x77/3TQ/OOG3Hr99pRTFr5FVe9+B8juaZuAciisNhQ+MDUporoJN9alPr6QpQ6dGuHPwP4HmV/+QmV3GAdKqmLH/gJ3bvDDPeTorF/XsAx977E5Ua96O8AGFa3gWMrf6JfU8OLOBw37S/B+T3BPbm3vWt6Is82mxv5//buOzyqKn3g+PfcKek9gQCht4QiAVGxgBLsxLpqXOvao+i6Ggu67nptKz/drLtq3NjWXmIvsWMQsCCCBBAShEDogSSkJ9PuPb8/zoReNYFAzud5eIaZuffOmcnMfe9p7/n8+Ay5KjJFoPrkr6gYnz53rw9kxrQuBoLHcj6c9+vRY1A1Ag9q9Pz/GtJGH4eqGfRtCAnj0+FHb6qJiG5dQvAt4C8V49MPyVaJvWbGCOAqVFdYay7wt4DLMOu8mDGno3K5J6Lm/j4MTMGsO6QvINtTXnZRWIRRPbXJTjjGwM9psY883Cd0zl93m+vBjElANTe3dsfMBCZh1i3c7tjRqEDdOpLzK+C6SfkZB313ZZuvEmWa5rJ5PQf2/7HfULrXr1rzaNQtEajFIxqBiyZklG1eRjAYiEcAo1FNc63LPm4A7ljw/OBPVyb1++7Dk/84qCUsghCft37iwh8iujTWOlD9CRd2phP93jBNM8sYvv7Np+KvJ8TycO2Lj/x67pyS1LTSkp3/oc0YJ2o6zQBUcF6KWVe/0213InlasZEoN14eTf1zy8UAAyCypYljyxbSp3JtrTAcF5im+VUbvLWDylYr5ZDQsJjHT/9LS4MzsrU/7Vngxorx6bsf8GjG5KBSNWJLHnqs9Lg0EOeiar6nNaSNXoUKHhcDrIzvuvGLoUeF2oYRjRrAd13F+PS32uP9HbTMmEhUS9zfUdMBlwDLgdOCW8wHLsGs2+Vyj9rey8sucic4V6ysDvRN7u5axMmx//xPhKP2lp0GZjPmGNRiFamoeHEb8Cxmnb3dMfuhcu0PQV1A3Q48cTD0F++NfZ4StRd+6VO9vv+P/YZSEdE9pbDxzBGZkR/9B1Uje/frov6XoGpgV6JWCtm6U96Dmi/7j+Kn04xV3fv++P4pFw/yhYQS7mkuP+/n6T3C/V4HaoGFy03T9LdD+Q92xTFrLbrFrWW9owfL+g4Z9KnfNzwNFux0a7WARwB1MtonydOKHcDLVaLLRVV0wS29DF+5nPTVy3D5vbNwOM80TbPzjDzd1mxseymGMbCxJYr8ojvkJSc9+aoUxsWoC9D+ydOKb6sYnz5vp3ubMecSDMjAXx8rHStQtQKfFRrxx+a+aVej5im7JchvBxz23aLufY9BCAOVNvD8ivHpB32toc2pVLsPYsbMQuVZHxz8B2rd79v1gK22Myk/w/dU9lengz1nnX+o8X3Dn24+Kfbf61HdKYoZE4pawOM21CyvCiADs26H1bXysouOQgXkJFQL3jmT8jNm74e3st+0x2Lzv8Q2N5JYVy1th5NCcdbDwEmoEbkuVDLwRaimt6GolHHfopqV+k7IKJtc/HSad01y76kfnHLRIF9IKGGe5oUXzimKD/d7Xagf0qU6IO/S0tqabk1H2qpFv3zwQIBL2/pFkqcVC1ST6UWGDHCmfJdJS1/giJVLcPs8eTicx3XigIxpmhLDyAPwxybxy4qY8I/fv+Rd4GxUF0EGMCd5WvGfdtw5ZhCqKRXgidySsctR+cbxR8U90Nw3LRe1qLu7xeX+7uWjT/16UY9+xwUD8kvAcTog74FZNxXoC1yBOvdMwKz7sw7Ibe+G/JPmgZEFsMxzLI1WwhTMmK8xY8ZgxgxFrU9wOyogq+Vgdx6Qz0HlxE9CdbEdeagFZGif5uvzgbcqwiKbPjjyxAhh2wFpGLGvyT/4UIuO34DqvP8atWLHhxMyyjY3QedmZYqKxO5vvXv6pec1h0cR4vXMv/inqWFuKzAI1b9w0sGw/NaBZJrmd2FDVx7zWOLNhFgebvjfwzVOy+qfU1D4m5v6c7MyY4E7gNXTjzr5w/lDjnzMGxJ6gZA2fyaXkf6f+fGHc2x88sp7//HwS3s4XKdgmmYcUq5HiJDwFYvpYlcur/ZFDHg0+8FxqM/ydNRF6ciK8elqMXYzxo06SR0uJdP/XXrsRzbGP22E8HXpMdOfkDwKiGh2h6z7OnX062vjki5CzUsOoAYyPXlITm/SDnp52V9PBzGuq6tUnhb7iIhwbHM6qgSuwaz7cMf9igTq9/IwKnB/CmRNys84JActtkdN+QeArs0N4VENtUjDcAJjJ2SU+SdklN2O6l+OmZBRduKEjLIXtw7IANWxSbcXnnj+ec3hUbi9nuVZc6eVBgPyGuA8HZD3yrzISos4WY3XEUpZ79Q41BJnv0luVuYRwOyNCcl3TT0u86lfBo9a6w0JvQApudz3P45kFlXre/qMxpZROiBvYZpmDUJ8CBCIiafaF9EvNXrjZRXj06ejlq37CggDXk2eVtyaneuvqIBc89+lY35tCIvK/WL8ueKJq//GnBHHjvU6XRHf9xtW9vKYUxPWxiXdhgrIy4CxFePTn9ABWeu4RDbQsMGfKt6p/r96SzpBZQf8Ghi3s4AcdDPq/CVQM23OOlQDMrRDTRnANM1VQM9vu/fll4EjAJ6oGJ/+5z3t97drLj320/HnzVzbrbdwez01p5b8dGf3uupnUEPix5mm+V2bF/YQZJrmn1yulhfKjw7hY3Eu/deWcO7HrwGcmlNQ+MW+HCs3K/MK4PmS/sPFJxPORxrqOi62rprzW963T+j6meHzhlibViT2v/iGGZ1yfvjumKZ5GvCpYVuElf1CWvg6f2aP0lGYdb8kTyvuhRqRHQ34DWn7hzYuC/+/pbnMKe/y9pvD/nD+4oEjtktKtI1FwP+ApyrGp+tRwlqHl5ddNBwoAhId+O7OTs76J2bdLrsi87KLzkTlhhfA3ZPyMx7ePyU9cNqjpgyq+Y1+FcFztJR/Sp5WHLu7HSZff1Vs0TGnf7G2W2/h8vv8XRtrz+heV/1Q8Ol/6IC8T773+8NIr1toA6zoPoimiEiA/NyszMjd77pFblbmRODpJX2HiE8mnC+lYYCUhSnryyddsfSDn8YlfW4AWLb7ch2Qd+lzYJZtOPAldmNJfZLr7VXDFi7L6bdwzbQTBDAONTDLZQsjfGHUIE4f9TR/P+e+8xcPSgch6Fq/iaSGmo1I2bqCVClwGTC8Ynz6v3RA1g4Wk/IzFqIGdGHhviWv4v1drhKUl110BGpQrwCe5ne09h1M2isozwJIqa/xJlZXgBBRqGkIO5U8rdgxfcwpc8r6pEUYlkVUY925E3+ZdSWqQ38RwUEu2l5bClR7VnQzBspSbOFg1SkDvUAf4OncrMxdjrrPzco8NTcr8+vcrMxV5T36F346/lzXRydfKKVhCOBVhDjr7OWzBg0eNOsIw5D4/e7pmROLX9tP7+ugE0wdeieAPzZJ2u4QVjXF8eGaIcNeWTFq2QuvX3P98qIJH8346bKFb/58C0eu+im48p5BUn0NZ8+bzjnzZvzjDz9P744QiahFXIZUjE9/RTdVawep11A5p5OAKcE+423kZRelofqOw4EvgBsPlSlPe9JezddHA99jBVrWBvxhH5/8R1BzK/tVjE/fJutW8rTiATH1m96ui45PF7ZN/5Wl95y46tcigrVt4FjTNL9H2yemaX4EnLFsRPQPU2Mzjk6SG7ju3X9a3qoQB+oHcf3WTdm5WZnRwCPAtYCYNfJ4Zh510taHfA64/qbv38wePPjbJ+Li12PbRr1h2CMmZJSV7793dnAyTfNjIFP4PCXdVs4pawq4J9psWT4r1PBjI/DaLjb1HoQ3rgtR3haAm03TfPyAFVzT2kFedlEmamoTqBHVOZPyM+YFnzsOeA8VtOcC4yflZ+xxnfFDRXsF5VBU/lFX+LIFvHz2tf6qhGQX8Diqo/4vQBlqkvhVAI6An6OKZ751WFP9H1FZpEYDL5imeWWbF7ATME3zduARn8P58evHTjjFI8Lcl9c/M6/L66t6A/GoaTmPoNJqDi5P6Z9anjIgof/KJazr2vObGWNOOSF4qJeBF86VBdOPWLX4/tjYinuio6uwbcNvGPa4CRllvz2fcydimuYA1KIgLuDclGUza5yG/WatL6yrJVWDlRUaQUuPvn7pDnWh8vz+RQdk7VCVl110MypFpgs1e+B51MDHS1CtuPOAkyflZ3Sq9MntEpQBTNOcAYwNqVjVvDYiOvydzD/tcts+q37lmLnTfu6xYfWRDWmjL0bNVWsABpqmuaFdCniIM01zDGokfPVn4w6btlL0O2+oXBC4ae2jQ1Z8kXKvbRkXr+7al8/G/4GAw0FTRPQOx3DIwGMvk/UhcIbPF3qi2+0ZAWBZDp9h2CedOGHZjP37rg5upmn+EzU/3w98BNhIGS2swGqk7CWdrhODc40rgStN0yw8kOXVtPaWl13UG7XoxPYLn78E3NSZasit2iOjV6tPgbH+mIQNfctL+g5dMq9y0eCRSagawCJgbY/15f3Gzp46sOf68o3AmQ1po90E8/wCD+mA/LvMBZqAhN4rat5Y1dc+b5E4zDmzx4SFKVesFIWca5cYw7eMKVBXZ5UI0QUgSta//hRXngHcAuB2e7Btg5qabuXR0ZUnnnzSrwfd6isdwN9RqzqdT+vKN0IgnduMdXkDuN40zbr9XjpN288m5WeszMsuOg/1ezgu+PA7k/Izvj2AxTqg2rOmnA7MQ8qmyNK5ARAxGxOTG5Dc+MhTz76cm5WZgZqfBnByTkHhV6ZpTkZNEF8FDD7UVxNqb6ZpvoP6sj/09rhj3dUi6fbtt0mXc+zelH/2CWf9eU3GEcuTpxX3OUz+HHonD70HpEkpZHV1imhsjKe2ptt7DQ1JF+psar+daZoClV52DKrrwI/K39sCFJqmqbsDNK0Ta8+g7ADqgIiQ9SsnuWsrHwPcqJqyCVyHSnzwdE5BYbZpmkmoJAjRwGWmab7SLgXrREzTvBiVNa30p96pQ0p793ikicgcgZS9Kf/8JnJHJVPRupjs7aiVoQ5HJbboHQi4mubOOTPC5wtvBs43TfPTA/NONE3TOof2mhKFaZoWMAfA2623BzUd503UnLP7UAG5FNXHBqppLxrVua+n2LSNQtRyi6lHrCwdUZYx9naESJTCSJqVce7EZCp6s6W74FHUUmg3Ar0ty2EvXHBShM8XDnC1Dsiapmntr92CctCPwdvjcgoK16MWRngK2IRahOLMnILCJtM0hwDXB7e9zTRNe8dDafsq2C/ZOu3gIoCK8embKsanbwKYkFHmmznj4h+rq1I29937/W5Wlo9g7pwzjcbGhE3ARaZpvrHfC69pmtYJtVvzNYBpmhOAqaggnLyzvkjTNA1U3/IJwIemaZ7dbgXqhEzT/ANqha6VQN9gMovW504CvgRJVFQVUopljY2JRagBYpuA503TXH9ACq5pmtYJtefoa4BvgA2oAS2nsqXWtrUbUQG5hd1k/dJ+s89QQbY3cBTBbGumacYCz6pNxEcNDUn/AmbqVgpN07QDp12br4P9yq8G796yk+czUHPUAO4wTbO8PcvTGZmm2Qy8H7x7VfCxI4CfUIG6HLjENM3pOiBrmqYdWO3dpwwqi1cAGB8MBgCYpjkcNSfTAbwC5O2HsnRWzwVvrzJN8xNgNjAA9Xe5wDTNTjdBX9M0rSNq1z7lVqZpvoRa1WYW8ARwNzA0+PR84GjTNFvavSCdVHBubAEqaUUrG7jBNM2nD0ypNE3TtO3tr6A8CChG5TXd2mfA5aZpVrZ7ITq54IC6i4GjgS9M09zVguKapmnaAbJfgjJsHun7KGou8meoNJrr9suLa5qmadpBYL8FZU3TNE3Tdm9/DPTSNE3TNG0v6KCsaZqmaR2EDsqapmma1kHooKxpmqZpHYQOypqmaZrWQeigrGmapmkdhA7KmqZpmtZB6KCsaZqmaR2EDsqapmma1kHooKxpmqZpHYQOypqmaZrWQeigrGmapmkdhA7KmqZpmtZB6KCsaZqmaR2EDsqapmma1kHooKxpmqZpHYQOypqmaZrWQeigrGmapmkdhA7KmqZpmtZB6KCsaZqmaR2EDsqapmma1kHooKxpmqZpHYQOypqmaZrWQeigrGmapmkdhA7KmqZpmtZB6KCsaZqmaR2EDsqapmma1kHooKxpmqZpHYQOypqmaZrWQeigrGmapmkdhA7KmqZpmtZB6KCsaZqmaR2EDsqapmma1kHooKxpmqZpHYQOypqmaZrWQTgPdAG0AyM3K3MCkAMMBZYDXwL/yiko9B7QgmmapnViOih3QrlZmQL4LzAw+FAv4AQgAbjtABVL0zSt09PN153TeLYE5PuBT4L/z8nNyjz9wBRJ0zRNE1LKA10GbT/Kzcp0AHOAdOC/OQWFNwQffwK4EVgNDMopKPQcsEJqmqZ1Urqm3PmchQrItYC51eN3AGuAnsB1+7tQmqZpmg7KnUqwL/mW4N2ncgoKN7Y+l1NQ2AI8GLx7S25Wph5voGmatp/poNy5nAIcB3iBp3by/MtAFdAbOHE/lkvTNE1DB+XO5ubg7VM5BYVrt38yWFt+O3j3D/utVJqmaRqgg3KnkZuVmcCW2u/Tu9n03eDt2boJW9M0bf/SQbnzyETNS5+fU1C4ZDfbTQeqgURg3P4omKZpmqbooNx5ZARvP9vdRjkFhQHgg+Dd89qzQJqmadq2dFDuBIKjrscH7xbtxS6b+5WD85o17ZBRkpqWUpKadmpJalrfA10WTdueDsqdQwpq/rEFfL8X2xcBm4AuwPHtWC5N229KUtOOLklNW4xKkPMZ8GtJatqfD3CxNG0beiBP55AevF2cU1DYtKeNcwoK/blZme8BVwMXsne1a03rsEpS0waiFl2JBCSwFnWx+p+S1LR1aaUl7xzI8nUGedlFMajzyWHAXOD1SfkZOnPgdnRNuXNID94W78M+rwdvz8/Nygxp09JoAORmZabkZmUW5mZlTs/NytTzwtvXo6iA/C2qBagX8FjwuadLUtO6HKiCdQZ52UUpqPS++cANwPPA6rzsoozd7tgJHbCgnJdd1DUvu6j/gXr9TiY9eFu8D/vMANYBsYBepKIN5WZlityszCuBn4CJqFHun+dmZeq54e2gJDWtK2r2AcB1aaUlVWmlJRKYjPpNxAMPH6DidRZ5wADUOeUZVEtFIlCYl1008kAWrKM5IEE5L7voLmA9sCwvu2hqXnZR1wNRjs4gOFBrbPDu3L3dL6eg0AJeDd69vK3L1cndh6opJAPlqNqbA3g5Nyvz1ANYrkPVVajP98e00pLFrQ+mlZb4ULU2gCtKUtNGHIjCHerysovSgTOBAHDypPyM61AB+gsgDCjIyy4KP3Al7Fj2e1AONlf8AxDBhyYAi/Kyi47e32XpJI4GklALUOzNIK+tvRy8nZiblakvnNpAsJn6b7bDiT8y9tXG/sPvaBg08m6pBh6FAx/mZmUee4CLecgoSU1zAzcF7+Zt/3xaackPQAHqfKRry+3j/ODth5PyMxYBBPuS/4iqMQ8E/n6AytbhHIia8m0APkfLaxWRK44A5gMJwNS87KJrD0B5DnWtTc+f5hQU+vdlx5yCwkXAj6gBgbq23AYkPOhNSKZp4Ajp6TngEukOeQuHY0Zj6uERttP9KeAGCoIZ2LTf7xhUi0QlKvjuzF9RtbjTSlLTTtlfBetEWrtl3t36wUn5GTXA9cG7OXnZRan7tVQd1H4NynnZRYe5BaclOgVfDvnvxR8M//d3L4y+a6bE/gpVS3g6L7tILxvYtsYEb7/5jfs/G7y9NjcrUw8M/B1yszKHeLv0PMrXJQWEEKhpZ4sBD0KMaxowvIftCikDegD/OaCFPXScFLz9MthcvYO00pIytqSeLQiO1NbaQF52URowGPABn2z//KT8jI+BQtSFf+7+LV3HtN9Osg/c8rorPLzuq5OinRwb6SR/9e3klt/m7hvocuPTY25x+A1v6x8kLy+7SDfftYFgf/Lo4N0ff+Nh3gTqgP6oVaa03ygQEf03f/zmQb7XAYmmaQ4FRgGVCDGiqf+wJCs03AYu1v3LbaI1KE/dw3a3obp3YlCB2d2upeo8zg7efj0pP6N+F9vkoFoqTs/LLjp5v5SqA9svQXn4S8NFVEjzgvGuhC5OIQhgWQ4MhrT045GVt3JsQ3rG80feMcpveN9HDch4LTinTft9UoEooBlVI9tnwXnN/wvevWV322q7lpuVGeOPTTwPIRB+3wzTNJ8xTVMCmKZZgmpmnYMQ0c29BntspxsgLzcrM+xAlvtgVpKaFgMcHrz79e62TSst8QAXoPK+jwTubd/SdRpnB28/3NUGk/IzfgWeDN59Mi+7qFN/5/dLUL60/NL8s+WgVKcQbBANa5w4IlBr9r7vkk7uWnsVRzUOH//y4X87xetorg4+99juj6rthdZa8s/BnNa/1ROADZyUm5WZ/rtL1QkFwqOuDkTFOQGk07VDFinTNJehBj0W43CEt/Qc4JdC9APu3M9FPZQchzrHlaWVlqze08ZppSVrgezg3TtLUtNGtWfhDnV52UU9gCMBuSxh7tfDXxr+r+EvDd84/KXhq4a/NPz+4S8NH7zV5vehpkt1+kFf7R6Uf77nw65nNo++xikEFbKloauMGpIyZaw3ZcrYVahReS87cPDXtdfIc2tPCC8a9GKCRAJckZdddFp7l+8Q13pSmfN7DpJTULgCeCt4967fVaJOKhAZcwNCIHyeFeZ9983f2TamadYDZwHVdmi4y5vUA+Cu3KxM3cf527SmiJ22tzsEM3u9hWqxe74kNc3VHgXrJM4CCAj/vKmDXv5mYEuvWx5YNSkpv+yeno+W3/q33PLbSt986PEVL/8j9/BJ+Rm1bJmedntwGlWn1O5B2WG73o0ULtFgSeYF6k5MmTK2ofW5lCljLVQqx/dd0in+VHkW91ZdzPqum2PIs3nZRfHtXcZDWGvT3V7PT96N1uki5+dmZQ5vg+N1GrlZmRFWZEw/AGFb/93dtqZprgKuAPAnJBOIiHYDTwUXFdH2zRHB2x/2cb+bUIPw0oE72rJAncxZAAu7TY+L98f0eHDVTfbopqH09nVnWMsAhrT047iGkX2Obhjx01Tz5Ssm5Wd8iBqh7UCd+ztlGuh2DcprJs8clWBHHSul5Bt/ZcM1uefP3n6blClj/aglAq8AVnbzJ3KpPx0jrAbUKNQ38rKL9EpF+yh4Ek8P3v359x4vp6BwAfAOaj7nP37v8ToTf3TCOXZIGEiJ7Qp5Zk/bm6b5McE5tZ7ufbEdzhOBy9q7nIeSktQ0gy0tRft0UZpWWrIRuDl4996S1LRhbVm2ziA4Jmg8wPLEn/s+tPomGW1HGKixLecD95eGrrh3SWi5N0yGiIGeXv/78e/vXIW6IKpDdb11yjEs7V1TvhZgrV+y2l01Y1cbpUwZa6dMGfsi6kf0faQdzglhkYQ6fQAno/obtH3TFYhA9QUvbaNjts7nzNS5mvdOSWqaCMGYDOBuamjJevvd80tS0/am9ed2YLF0uvB064OEx3KzMru3b2kPKf2AaMDLbxvk+Bpqqo4LeFmPxt5nZwOuRndtzQktQ+nj7S6ACuCMlClj30mZMvbeE83L7v9n95dSp0fNbXTgINmX+Oz4BO8JbAnGD+RlFw05QOU/YNotKK+ZPNNpI88DWOWzsYzA//a0T8qUsZuAUyVydpQdzpHRkliHAPhrXnbR2e1V1kNUn+Dtmn1NGrIrOQWFvwJPBe8+qReq2LOAEBc2xSUMBeixZm0Yat53dUlq2nclqWnddrWfaZotwEVI6bOiYvHHd40DntfN2HuttetmQVppyT5//4O5sa9FNWOPBO5vw7J1BhcALE36MfwPmzZfv9+eMmXs8q03+uzaqeV5yW8eMTPqZ58Dh4i2Il/PjDUGAZ8CIaiZOJ3qPNOeNeUMA5HgsyVljo3+XrVp7+/NTilTxjYIxOk29pI4O5JjowQ9XAKJfDUvu0g3I+291gXcy9v4uPcCG1AJATr1KMk9KUlNE0v79HwkEB4JtkXqsrI3UelOQU2Bml6SmrbL2q9pmvMR4hYAb5cUAmGRpwK3tnvBDw2t/ck/bfOoGePCjPkDZsynmDGbMGMWY8bcgxkTtf0B0kpL1gPXBO/eWZKapgee7oVgEB0PEBneGBJjRSKRFaicBzv4/qpZpU8mv3HyB3FFAQAHjsmnxTjqBVShuuD+uZ+K3iG0W1CWUl4OsNZvUx63eP6k/Ay5t/umTBlbbWAc4xHeL5w4GB3hZGCII0IiP8nLLtpl7ULbRp/gbXlbHjSnoLAWmBS8Ozk3K3Psbjbv7Pqv6dkzBcDZ1LD8qDlz/ohakWgwsBI1/ePdPTSN/hd4EyHwpPTHdrn/Lzcr8/jdbK8prdMBtwRlMyYSlUTkHeA0IA5IAx4ASjBjduiSSSsteY8trUOvlaSm6ZXt9uxIIMzraPZmNKk1PgTizZQpY3c5LfO7q2ZNfzr5nbMe6/aKZWHhFsaFE6KcJcFmoRvzsosu3R8F7wjaJSivmTyzJ8Ek5GV+D4uSZz67h112kDJl7KZQGXJ6pbPmBYChYQ6GhTp6SezP87KLYtu2xIekPsHbFW194JyCwneBl1DfnwLd17lzlmGcWJukMngZ3pY3QTWLppWW/Iqak1yLSoO6ywEtwQQjVyPlfOl00dJzoEM6HO/paVK7VpKa5mBL87WaymHGHI9ajWsc0IBKY3okcCmwHDWo9EvMmNswY7bvIrgVlREvDvikJDVN5yXfvfEAzugK16jmNCQywJbkILu08PKFn34Z+8PF/+jxnPQTIMIhxh4f5VgaDFLP5mUXdYoKQHvVlG8XQriqAjazEn+gLqxyh5yneyNlylh75INnXvlr6MonAAaEOjg83HUY2F/pwLxHA4K35bvaYM3kmeFrJs+8aM3kmTevmTwzeR+PfyNqAE034OPcrMwdmv86u7I+vS63XW4IBHA21Dyx9XPBfMutI3z/vrsRvqZpNiFEJlKus0PCaO45MF4ajq9yszJ7tmf5D2JHApFAbd9TN67BjHkblft9BCoH82mYdX/BrPsJs+5V4DDgOdTMgkeBJzFjNk/HSSst8QLnAKtRrRyfBbOFaTt3vAEcJ5Jb48vjKVPGlu3NjgsvX1jwffT8q+/rmY9X+IhxGAPHRTmqnIIQ4OO87KJDPqGLkHKvW5X3yprJM91SygohRNwPjQEeH/rQipnXT+33e4/73oNPPTCqccg9ThxUB2xmN/tKfbYjY1J+xvq2KPehJDgYqArVVHp4TkHhDlOilvx1ZsL0Ls75b/Z291gRYXDFcl/NlSt8GSlTxhbvw+v0B2ahFiufCUzMKShs2P1enUNJalrU1+OOra3qnmK4a6uq7/73k4lfF/VPRyVfiQJWYVMa+7LjcgTp0k1VyxH2Nb4B8vsJGWUbd3ZM0zSHIuUMhIg3WpoIW7NspRHwZ+QUFC7f2fadVUlq2n3A30G+lXbh+gRgggRZF+18Z11y6Kvrk0PLgdIJGWVbFqhQteNb2LIowlTgQsy66q2OOwSYgVrVbg5welppSeV+eVMHibzsIrdE1g4Pc4T1D3HgEb76UOnulTJlbN3W231d1D8cuBLVcgEwD3hnQkbZUoDhLw2/aljzgGfN1deLCDuMJku2/NAUCGuyqQVOn5Sfsa9zzw8a7RGUzwA+8tiSV+xlvDf8secXXr7w6rY49gsPP3LTsfUjHw+XoXhsyZwWX0213zh1Un7GDvOfO7PcrMxeqD7LABCZU1Do3fr5/l/Nix3QYJXOj3Nus0byQ/NbrFMqAg8CD6dMGbvNPrt5rdGoE1gMaj70mTkFhWvb4n0czBanpl357rnnPE8YDOw+fU5S34plwLmopRn3ZA7wPPDKhIyypq2fME0zHSmnIkSC8HkJW1u2yeFpPjunoHBmO7yNg1JJatoPwJiolJb/pBxXc/OmWJdv/tDo9bZD9N5qszWoQYsvTcgoszY/asacA7yKWrVuFXARZt13Wx07HfgKdSFaBpydVlryS3u/p4NFXnbRMaGC706MduIQgkrnpnM3ZdzaE/Xd38SWOeNXsWUw6tYeB26dkFFmDX9peFY/T8or5urrXUmBOPxSWj83W44Kv/QAV07Kz3hj/7yr/as9gvK/gFtWeC2eTnqfn0L9H3k3TlwOPF4+ZeLv7t/89yN/P/XY+lEfdQskuKSULPdZconX/4Dfdj44KT+jTab+HOxyszLPAj4AFuQUFI7Y+rk1k2eKGw8PK56V6DzMYUu6t8hnK0NFtMchsiL8kldnNdGzWS4BTkuZMnav/l7BwPwZ6kS1Abg8p6Dwi7Z9VweXL04Yt2DuyUcNHzb8ayIitqkkfIZKzp+CajbtIbz4XCvFKCtOhliJqEZUpRp1knpyQkbZptYHTdMciJRfIERfbJuQyjW2a1OlKZAP/84c5we9ktS0SKAGcPY7fWNxQ2+RPn9YtAwuldmCWpwlFpU1CmAhcDfwyYSMMnUyNGMOQ2WWGoCa5/8YYGLWNQZfIxU1Zadv8Jg5wNNppSX2fnmTHVhedtFfh4QaD/YPs1nR7936QP/Pl7BlJPz2NqA+2wBqNa/WVei+BC6dkFG2cfhLw8cm+GPev3vtNQlDWlSD60qvzSKPhV/yLHDbblafOii1eVBedeeMOYYQh89tCvDI0PtZt/ZPSH8iqCwtJ5VPmfjTHg6xR3c9Nqn76MZh3x/VNKw3gMeWlPi81Wu94nJLGp/uy0jvQ1FuVuYDwD3AizkFhVds/dwbud/feFt62BOWIbjxV+9z91x31DXJ04pdSPkNQhzTvTlg3f/TGofwN9atdFTmbzTqioC5pmlW7/TFtrxmP9SFQGsKzleAu3MKCte0/Tvs2BYMGdp3+pHpy1POXkZ0dFXrw42oVKVTJmSU7XDyLklNOxz4wYqUrsbTrZlNx9s9ELR2+zQBzwBPTMgoWwFgmmY8Ur6KEKcBGC1NhFSuLXM21V+XU1C42xWRDmUlqWknAV8aLrs25vqa2EWDI0EIgLeBqyZklDV8XdQ/FDWD4B5UgAY1kOtBWoOzGRONWoilNZPaOtQUwJcx6/wlqWmJqAQjrUsNfg/cmlZa8luXSD0kPH39V9+eFOM4dsPof9EcX7L5cfdS8bO7TEQEusg+OPA5qoUn4hvHBmeVWAbMBqaue8LXBwcvAWGo7rfbgZf/sjq8u8t2vn5p5Rlj/7BpAgYGXlvyq8em3GevtWEy8Mak/AxrZ2U62LRpUF4zeWaELWW9IYTxtnctzw17nKald/8EIgq1jGAAterNY+VTJsqS1LRQVDNRTVppiSxJTXOiahAhqJGp1YAVnMi/jeEvDReXbTzj4ZNqjrsj0Y4SAM22pCzQsqnCb5vN/pBnJ+VneNrszR1EcrMyvwYygOtyCgqfMU1zBDAuzo6onTlo5HPf9kh096prqD29+Ov+qEn+DzS6Q8M/GDkuvDE0nKiWJo4oL6F/5VocW74fC4EvgM+Bb03T3KF5OzcrMxyYghoEJlDZlF4AnsgpKPxNS0cejD48/phXVp3R75Iho35AWsJOrPOOSj9v9U4XodhaSWraJaiLGaQh39t4v/9TK54/o2rUABJV034B+GTmjEs9SHktyMcQRhiAo6kBV11VibOh1hS29UFOQaFvZ691qCpJTfs/KeQdjX/20jB48zjWN4ArJmSUbfOd/bqofzzqhH4jKhCA+vyv39xtYMZMRAXn1qbWVaiRxC+XvNm9EpUW8iFU9jxQtbz/AF+klZYcEkFib+VlF4X1dNPQa9injuoBW9JShCwUxP/XiWD7Qe3b8hmOmtr+IWt9f2pMMeICscGHFwKPLPEY7/23MvTaoc39H7qx4o/hfbxqwofHlqzw2qz22ctaJP8EXp+Un3FQj2tp66B8A5DXaEmmRH/M7KSFvzSX3zQKCEtsqX39yIrFEw+rKqNf3boNSS21/lDLnxLcdQlqlPA4tvw4WlnAemAZ8AuwACgGFqWVljRf9eQfu46pG/nW2IZR4yJRiV9sKam0bLmOhuW1MvBSU1NMfnZ+RqcYkJGblemUhqPGH5MQaYWGXxKITfwe1dcbawnBi8ecjt/p4oz539KjtmqbfetCIyg87Bi7ISzCAIj0+hi8cT3dq1eTXF+9dYBuBqajRrTOBOaZpunZqgxHoCb8j9vq8HNRTYKfo5rVD9kT1jPXTWzude7yMJfLR7c1PoYsr18KnIRZt3JX+yRPK3YAo0/9YfpV/VeXX+kOBBwRLU0NXpfrpZiJK+f1iV96oVBNfK0aURdJX2zc2GfBkkVHZ2M4LkMIA0AE/Dga6zwOT/MMR3PDSw5vy6fBOeaHrJLUNIGQy5vH2H1qL1VfL0vyvUMwbpt+4+18XdQ/GdUEfQuqWXsFKqXsOxMyyvyYMaGoFYzuBLoEd7NQq099tOnXiAUbfo6+HMRlbGkWXw+8h2rmnplWWnJQB4q98UT21FNGDZnxuWfYawDEvuDAXWYgagQ/Jg9jUXwfyqO7YUh7ucsONAcM55AejZXGsOrljKxcSnhAXTNJh6RpvE3DqRYyXB3b8hnepqbIbzaF+N55sdpx9DE1x11+YdWpjqRAnNpHSqoCkg0B21cTYFqNJV+T8PWk/Ix1B+TD+B3aLCivmTxTNEq5PlKIrguaLR4cei/Nrurr33jEWgrcKuE0wR4ulRQv4EHlrd3d9jZqoMViYEltbHj1+jGZ5/YKHT06mdhtFrDw2ZJq6bdqaNlUb3jKW4Sv2CflTLxR0z2eyDWT8jMOmb6gRy47/ypP95TnPP1CCdlgIK0tK8/VRHejYORRuAMBzxXfFTqEyusLql/nVaAl//iz1wI3IeVNCLF5IJjbskmpbyS6qVpGN9WK2JZGojzNhPs8OGwZQLAIdVVbCixDyhWha8p6OhvrLhXITLacrADqUYOZioES1AXXSmDd9oPSDjavn3382eGXNb4fFVuLvy6CkxauxGGDR4g1U+Ljrnw3OnIdsGbh5Qs3dzQnTys+AdU8vcu5x6FeTyDa21Cd5K7w9HYvj+sh1kYnUEk81cRSQzjNVbblWFRT3S2mpqZbWos3JsTricTnC8O2HBheD4bPUysC/uXC8i8SVmCe4ffNdTQ3LBK2vSmnoPCg7/L5dcSgUZ44Y25Vjh8ZAbObHLxd417ll+IJIAnohTpv9EElF3GiugaqgHXpYQH7vDjf6EiHqhh4bRor/MbcyoD4cZ3f+KGh2lr82PqqE2Js+yrUtKutbfLUOhdVLogOb6oISZW2iNjqORv125gHLAJ+RQX+NUDtzloCD0av5V0/Lznty3QA9/JQoh4XFB8+mP/E/4Ea1y5nkC0AVrosf9hhVWX9+tWtcw6oXRs9oG5NTLJdJZqOt2kaa2HHbdlB2uDfFOKrrXd6rUC3sB4to529Gofj9MRjWKEIBD5bUmtJ6uxAc5MIrPTZcr4VcM1qtMXcZptlwMaOet7f56A85aHbP6tPiD5h+8fdIsTZQ8Y5M8r78axYQKPvWW6a4W+2G5zhW21WPD+x/y/fdT9swMaw2L5L4nq5fA5n/OgNS4j2NVEe3e2nkvjez1iG4/tTyn9c1ae+ImFZbI/hcZ6GYd2aqwd3a6rq261lU6+4lvrkECuwTY1aCol0Ak6QMYn4+x4rRdcBIiI8EsOQSGEjhQXCRgoJ2CAkfmnjx8IvAwQISEv6pV/68Qu/7RG29DjAK8BnIANC4BdgCfV/2xBYQv3ibAESgQQkSCkEoY0tKyff8XDqPv9VduP/zFv/Upcc9/DOnpNIozEc98LkAZS6hjAssJBxs0oID8TKrpFHiqdSo6pWRDoSgdeyp3/wHurq/0vgn6Zpbv6Cfl3U31lNQvpnZF74K2nj18uUoc1G2C7zz4ZbTURYzYTZHkItL27bh9v247L9OG0Lp7Rsp20T/CectiWELTGkjWFLDCkR2DhsCSCFkDYy+DGCFOrNqecIXqkFv7bqExebv8Sbr+IkuL3+pvtuvj/xN33Qu/HX/z7QInd2uSjA6lITKmN8jPVPp65EbHgnwvvgMS2e+z6MjIivd2xzrVgjYbUn8sSwxrhLBiIcCLtZOn3lAWG3+MFpR3kiXbYzzl0THb/Hi1mn9BNFPRE0EkETYTQTiocQvLhsPw7bwmFLDEsibDBsdav+SQwLKeTmx6Rhq09YSBASaUiklEIKqT5jsd1pQz2z5YNQ96R8+Lq/b9/y9bvd95+/V3lDXBHbPx7mbAhJ6TVH9HKupKoxhv+uTaA6vGKXx3EFQojwxxDqj8BlheCQLkKEZGTieg5LqiDcte24UcsWNPrdNPndts9yBqyAU8qAMIyAcBq2EMIWGJbAYQvCG5xENDgJaXLg8DkRQiCkAdIA1ZiB33Didbikx+2yW1xO2+t04nc68DucMmAIaQkHlsMQFga2Ydi2EMJGIAUSYagzmBDqhAMSIWj9Xqo/hiB2w6a/3mb++19t9dkDPPToXSVNUeF9tn7McNhGQv8F7lQWkbD0CKJemE3fo6oIjQvgl46AF9e/LRyzKmWMcYbvofAWQgYC75VPmbjTFez6TP5EXLhk6uhuTVUXJHjqxnftUZEaMag2wpdmYXXZ2R6KCLhw+KNx+CNx+MMxAuEYVgjCCsGw3AjbhW05sW0Hlm1g2UJ6paAFQ3qkkF4H0ivV+T0ABBAyEDzHW4ClPndpA6jHpZSGAInd+isVAomUIIjc1DDjzrsfOWVX5d3pe9jXoHzzy49YBT1P3mXSkTH1v3Bh/lOMWFGjXsBpE57oq4lPbXo/Mtn7KGZd6dbb95n8SRKQjRpE4QRwG14uSvmIITFLiaGJSKOFUOHFcFjYDoHlUB9OwBBYhsB2gnTuuRJuYVBJVyroRiVJVJNILXHUE0MjkTQTQTPheAjDK0L36XPZmRPqZ/HmWdltuoDAA//8a3ne4ef33vOWymH+Epq8fSmL3Px+LOCkivHp074u6j8MtQzj6aiaawWqRjEM1VIBqB/4OnqwjEGsojfrSGEDyVSTSEB03DXg+wWW8/1J57b5Ag7di+ZKW+x+NdE+9ctpqHsUQzZufmxMwyAurTzPCrHdjspQN0+mJlEcr/4uXesWcfSa6aR4o0kIxBIXiCY2EEWkHU6IFU5tSDjrw0NZG+FgTZjB+jCDijDBxhCDRlfHXKPCkBbrMg5v88Id89V7crlz16kPwuxmshZU0/fXcFaFVQTqwlc3FSfOnx/R1Mvq39Q9qWdTd1ekNy7JwNj1al0iQETXUiKSfyEscRkhMeswHLuf3GEjqCYxeH7pyiYSqAmeXxqIookIWginhTC8hCD38B1qCzfOfav8ntv+sbOpR7/ZhR/my2+ix+z0ubH+b7nv0Y8YfMRsHO5dxpblqIyAj2PW1e7t685MHxO6IrZbRlhyU1Z0cs2x7tiWbkasP5R4y7Di5eambgnUE8N6urOB5ODfIZ46YmggmiYiad78dwhlT7/l3+PC1V/a/77sjn16gX1eRDq8xRfoG1ixw1xLv+FgjdGLWdHDGDlgBIeVTwvE9W0uThrRMMoZYsehJopfiRkzG7i8NTiXT5lYCTzQZ/Inr/UR67OvjX7/kl7DFnfzRmyJ+x7Ag4NtW0B3TUqQtqDOjuUXMZxfjTSWiwGsESkExL6vwGZICwcWTgI4pIWBjQN1K6RUt0gMVI0PwEAS1eJp82YphxWo7htYscug7JQ2XT0Slz+aGXHxLHClgQtc0ucJo2VqC6G5L3JR5NdFLEbl/W111HaHqgPmA6vWNEfGVXhqx/iNb+MTxbciCYkhwEDgF5HYJGKLeGwRiSUiCRBGQIRiEYotQ7BwETCc+HFgCQNLCGwM7NZbBLYQW7U0qP8T/H8rKba+v9Xju/gskj2bdvHM79PXKsfeRTK8lY5e2MJBeXQ/ElyPEai4e77TqKz/x7qhQ0fWXxcPTsf0JAf3HBZGi1MQYkn+sNrPzUt64WDX6X27BSCtyYZK9f2ykVjSwsKiRVjUuqDObVDvctLgctDodNLsFLS4/LS4/XhdAXzOAH7Dxu+wCRhS/RMSy5BYQl3pb2n1af28t7QAsVUtbOu/y9b/3fpvYUjJlmyXbSfZswkZumOs9wsnaxw9aTHCeScVnquqYGhzinN+VbeYUZuOHGcAHltdlbaykY021AbA6xcYPiHDIhwiKsnhDOvSMNxIahpB/AqBU0gCoZvwh1ZjhdQRcDVSGWpTHBnP4rB4loQkstKVgM/Y9/OLkLY6t2DhCJ5r1DnGRkh783nF2ObXIYMtFq2f+Na/jGB7HWBYVtWOr/j7xDU3y77hK7b5AzSJCDY6ujDTeSzvHbFg8T3uH/NRo9NrURWuzGChBqGW1bwPOAMz5nTMup2P9zFjQlCzOQ4HRo49m+FjKRnClhHzUAstdW6+rhjNF5FHMj9xKGtietDs3qEhZa84pV+d62Vgy3letn7+rf+2P1MF2yVka/vElr9BaItvn6fptlmf8mOPZTsWpKX4PwvJFF1bKvjf5DuGH75gwS+YMd1RI4H/iBqo4kINFHocNfjHgxoslNkY7njq5xEx0X6Xgctny/gq/684ZZnfZaz3O42NfpfY4HMZlbZD1AMNls/wrP2+66jmjaGn+r3uDF9obGRdXA9Kew9lWdeebIiOb50OsZnDsmSkp7kp1OepcQf8G11WYJ1hW2sErLENx+qA07XOMowqjyukqiE0vN7rcrdUjE8/aOZ+Fl/xp7hQO7UystuxjuIuNbwytAKXq5pMPiSJHb77Nmoa0xOofrZQoBJYeceMe5fW0nxz36S3Jm+IrNzcBBll2Qzz+ujiDbHcgfAGIxBRKa2wtbYVvsK2wle6mwc0JVV2PxERf4w/JHaH1JtOfxOhnmqf299Y6wy0VDos73qH5Vsv7MB6QwbWGba/wrCtSiGtjc5A86bIxrU10Y2rPQdLv9tXt31z5ftDwp57r2+IAOhd4+fMVb7mMzbJ8HifZFF8KVeMHm3ZQjgSPfaS//zcUjW4we6CGkuxCTXgcTVqoNDGeQScn+AfWYY1ZhPyiBZkqA8Ip5FMYzbHBjbh8PZltW8kPrnjiSjUqPWFGHV1LtFc6RTe9U7hW+sisMopAusF9gZgo8RR1WTFVddZ3eqb7ATvwTql8P47b3f6QxJ/fXb8KX0BTiwt5oGVvXFtVffwSck826JR2MwWFhuETQiCbhgMxuAInCTu5ILLQtp1yNpFkaLm8x4ux/wEI6kiKmSHD9ywLWJamojyNBPpabbDvC0tIT5Prdvvq3IF/BXOgH+d07LWOC3/GpdlrQ3xe9eGWoENToezTjpdnq27kQ42o9/5vHJNQnLi8BWLOfWL1/vvNNOcWhTkQuD/UBkHq1GzCRaignY3VCrTdFRA3llTnPQI15oX4zN9b8ef2GVJl8FRAed2m0lJlKeZ6JYmIj1N/jBvS1OIz1vjDvg2OgOB9a6Af63DttY6bGuNKxBYE2IF1ofbgfVuYbQA/mDO+QOiTUdfv/DRic/fF/nAlR4RRvrS4ns+v/ZPD22zgQrQrwEnbL9vwIDZh8fREubA5bfL3D573JgzVu505Fwwz/L1wE2205Xii09mY/c+FPdJZWmXFGxjS4061Odd7bID0z1O92d+p2s2sLxifPpB+8XfGzWXdLu8cuVRL4roXnwzuD+xgxYR2W0h7qjN2RslatrGPyZklG0TqftM/sQN3JwQtuDv7pQ3Ihudqs83o8krBzdEL4pq6vbcejv5ldseem6bauh/r/n8BLev4d+esITNyUqEHSC6vtyKaK5YGurZNDO8uaIwrnbptMN+mXtIj0TNyy66wuji+t/D4yLxqfXA6ddoccuCufzj8L6sD0nCaPT97Pqx6jYRkN+XT5m4zeC2PpM/GYTKgPQHtqx2BMBwsbz6L8bHzVGeXj0Wt5xiNNlb1kZwiWZPpFG9wCm8RY124qctduy8SfkZjXQiuVmZ/X5MH1s2Y8wpOK0Ad/28Tp61KU5IKW0RHJm+JxbS74X5bpjmRMxeGmmUXnZ0+JG2tG6wHM4tVX8pSWyso1tdNV3qq+2Euk3L4hrrvhIOx6c4nHNM09xputRD1dg3P75hadeeee6Aj2tff+y/97zwxg273NiMGYKaOz5kD4dtzQL2M7DgvcSMdQ8l/im7KrbLud6QsM2RONzbQo/aKrrWVrXENdQWJzTWvh8KXyBEqWmaB9W0wDYNyl8X9R/8EleWfikm0qt29cbZ55zRNfj4COBMYGVEU+CdMXNrT0OddHqjRkT2XNI/gjU9wkDK1QgxakJG2Q7NLrlZmU5UMP677XAm+hK7Udc1hdl9h7EkuRcyWCt2WIHFluF4GiHerRif3ilTPpakpj0FXD+j+2Eti45JDxtcdRSB6GWs7P/T2qUNA99fWDU0p3zKRF+fyZ+EoZqu3aiMOhemhM/t7un5Fl5DMMDrk1fVeJ7JbKnNwaxr2v518rKL0ty++hd97mg1GlXaxNUsseJqf/0usfqX3MimdZ/9lkXmD2Z52UUG8Mym7q6rZqSGWguTXNv0u/RsWc85sz7iGV8mqJG/01Gjz7sCRwNbd5jawHeZxg9z7nO+Mrq8OWPsvKZz8MpIAJx4mtxG81vNdvwzwOyOOqJ0f3ro0vOWvHDhzYMaw6NwBQL24/N8dx6xyXoetTrUVajzThgwEpXHugU1EnoxUAR8nzJlrCd5WnECcENwJkISgLBtetZspF/lOnpXrwuEe1o+lw7n0wgxdetpgZ1R8rRiI8zb4msJCXOcOeND7+DFPyXtNhe+WvTjHNR5pxeq5XcjsBRVc/4ZKMesk8nTiqPiaiv/XR8Z+yfL6TIAIrwtDNqwmr4bV9cmNdQ+KwzHy8CiA1nLbQttntHrP++dU/1w3L3xLumjd+XG6PuSbu6JmgrQ2tmyGvjXhIyyfwNgxhgreoZlLe8T/gpCOICTJ2SUfbX9cXOzMg8Hnpcwwh+biKdLil3ao7/xQ/9h+FqbLqT8CCEerhifPqtN39RBqCQ17UjgRwktd5551l3Ht4z5d5g/ml+TP2WGZyQ1xM5CLWV3EbB56cVE5ypC+z1JncPg8BZPU86m2mOH3125Q+KLvOwilyPguddyuO5COAxhWyRv+NGXvGH2k3G1Sx9MKy2p2X/vtuMJLvS+AuhW2sM14+1jI0cgREyyp3LZ67/cMWBI03LyA5nNjwXOC/fumA7bj5oD/u4Dzv/NutQ59bZV3vRLptdfR72lFvNy4lkRwH0vGAWT8jMOqppAe8vNypzcEBX78NtnXk11VCyhPu/U8lOOOmnPeyrJ04oHoRKKXIVKbkRkSxND1peTWrGKcG/LJoSYghDPmabZqb/n2xv5/tRZ62MTjxq2spTTPnv11pyCwsd+z/GSpxWLUE/zxbYw8n0hoREASfU1jFy9lD6Va0sMIe4F3jdN86DpYtyTNg/Kz5kn3PzYuL/+u1ok8RfrEe8Rxo+tU2k2oq76W5cIfBSVDOQE1NVSLPDhhIyys7c+Xm5WpoGatH+/7XQ5Pd37+ptjElzTBo+kPHFzLJkL3FQxPv2QXTlkX5WkpgnU1eZQ4LZvzjp/hF13wqUSm5Cu/2OufyCf25unWlpARapjSXFMr2dOXBzqCunn83uvra0bPPH29TskvMjLLurpCHg+spyh6QAJ1b/QZ+Vnr8XUl/85rbSkfUZXHYTysotOQeW5DmkIFeUzh4bd9ck1Y97EjHkElUKQTTJy7kneR9+vJiYW1VRXDHxbHnqRBdzqtcPu+q7hyvCSlhMBEAQ2SJy3ozIXHbIJWH6P3KzMeGD16sGHh795wtmt40qKgUcqxqe/kTytOBawKsanb67FJU8rPhqV3e5YtsrVnFhbxYh1K+hfuQ5DWk0g7kWIvM5eK96VER8UXbEhJv5/US1NXPt6bsntr763p+bpXUqeVpwU1tL0ektYxIkA0c2NHL18EX2q1lULIW4DXj6Y++B3pc2Dcm5WZuj3Wac0fxc3Vpwgp3IN/wXVRDcSNaL3AbasI7u1TcDICRllq7Y6VjwqqcVp/qg4PN37+Kuj4lyfDz1KNoRFCKT0IcQ9wL8qxqfrE9R2SlLTrkKtE7uqPrJn/28yTimOrB85FGBUxJssDGtcNiXwx3/ZGP+bF37JmDu6JH41KyzUFW7b8pyGplMn/7n8y+2PmZdddIywA59KwxnjDDQzYNm71ckbZp87tGTRjP39/g4GedlFp6MG07lQF49HTsrPsDFjLgSeRa37WwJkYtYtx4yJQuVbvqvCN6jHV3W3BGvHUoJ4HLins/UT/xa5WZlPW2ER13477ix+6pOK3NKdvBGVlctCpd+sBMajBhYpUtq91q/0j6goD+neUBucFy9fRojbTNPsFJkBf6vkacVRQsp6KQRXFL5I4ppl6TkFhXtMMbuT45zqDPjfDDhdMYZtM3LVr4xa9SsOab8HXGeaZpuPKu8o2jwoA1xp3vvDp8efMybKruc/VvaHIU7vgxMyyuYAfF3UX6CSwV+NShX4LWrqzddbryObm5WZBnwsDaO/J7mXFYhJdKyM78pXQ46wAw6ngWoaPK9ifPpOJ59rUJKaFobqLkgAsq64NfGj9LUZ1cM3HB8OcGrs/9E/dNZM4NOHEuIefDM6yhFi2/Yor/fcZ7KXfbj98fKyi85D2q8jDFdkw2qGlrz4Q0RzxZlppSWH7A+kLeRlFw1A1dQigEsm5WeoPIRmzLHAW6jugwAqr3KKLQ33z03nMrvxQiQOQK4Ecemk/Ay9PONeys3KPEzC/Jbeg6mPTWJhj372/J4DpRS7nJTqAwpGlcyJPGxN2Vmh7hAVxW17LYZxqWma0/Zb4Q9y/T7/YXlzSFjfjIXfc/h3n+bmFBTetrf7Jk8rDkfKhxHizwDxDbVkLJlHYmNtACFuBZ482PuM96RdgvKDl51/wTOX3F7gc7k5adGPn7xy43WZ+7J/blZmTwlzAtHxXbxdUizpcjsWdesjZw4cQXAJtmnA+RXj03e7cpEGJalpD6Ly+G4Chl9wl/OSo1ae8X8j151IhFElL0m6QcyMcPLnrkkAxFnW+TOuXPzO9sfJyy66BimfRgiRWDWftNJX3nQFWi5PKy3R/Zl7IS+76C5UopbVwLDNy82ZMT1RrUHjANb7BjO9PttTHejTmu3lTSB7Un5G3Y5H1XYnNytzuu1wjmvpPXiFHRLWt8XlZlV81+dqwqOmFPcalAxMBEKQcuEFs7/qktBQe7d0uVU+SCkltv0EDsddpmk2H9A3cpDp88WP//K4Q25J2bSRrPfzNxp+X++cgsLdNvcnTysWwBlI+S+E6A8wfOUSjlq1BKdl1SHEH0zT7BSrn+1z8pC9EeZtKey3eqlV2m+ooyImYaJpmuNM09yr5s3crEyX7XK/19Kjfxc7LAIJju8GDK/9pUf/2OAmLwLXVYxP18Fg7zyEOvmkA9cA/5qT8vmdA6sOj8eXKIqaLvrhgd5TRwMup5SP7SIg5wD/RAi6r5vJoKVvPWtIO1uvH7tP/oP6/PsCH+ZlFz0AeOH900DOChX1awMy5LgAoT1R88XrUCsQvXqwzhvuAB43rMC4iOWLIhtSRz0Z5vfdOHjD6quBq8esWFyIWkozAin/hhBjpcsNUmJ4mn+xQ8IuNB94YNEBLv9ByeMOeQIpb1kT34X1KQO69Fix+M/AI3vY7X7gHoQgsqme40t/pmdjLUAFQpximuaC9i53R9EuNWWAP/39b8Wfj//DiEhPMxf/+OUSASN2ttzf9qZce/mz3qQeV0uXmxanq/Gj9LHraiKiBwWfNoH7K8an65PUPihJTbsYVRtbCfS/4C7n7UPXj314bPl51IZu5M30f4CQy4GhCy9fuPmKNi+7SKAy7/wNoNeqL+m//MPnBVyrA/K+y8suOhb4ih1XQtuaD7V84N8m5Wes3y8FO0QFp1CWAb0kPNqYNroYuAuVRnZbtoW7an2Lo7nxojtffPOD/VrQQ9CQwhlzNkVEH96juoKL3n7SBxyRU1C408CaPK34atT4Cg5f+APpleuky+EQqO6cCaZpLtt/JT/w2qWmDDBwRclnU4/zjWgMDac6MmZwYmPdragr0x2YpjkGOEIE/CfLbn0yEYIF3frU/jBwhJBCDEJl/bq6Ynz6a+1V3kPce6jMOb1RCSn+tSxx7jFHrj79jFhPFwZVHlH1a5fZl24XkMOBfFC5H/st/4jeq754W8B1OiD/NpPyM77Lyy46AjXy+lTUNMGvUF0LTahpUN9Pys/QI9jbQE5BYSA3K/MW4F0Bt0eVzMkFRjSkjR4APISUJwsrEOpsqHG7qyvqDL8vI6egUI9RaQOJDbXX1IZF/Lw2IZnVPQe6e65eejcqk9c2kqcVZyDlfxGCMcXfMrJ6vS3VPORlqIC8aoeDH+Laraacm5V5+gcn//GTpf2GMmrlEo4sL/Gi5sTOABpM0/SaptkD1axxUet+EpjbrW/dnEEjWtf6WgBcVjE+fZ9H8GlblKSm3YtqaQgAJ1xwl/P7P/58z2Mx3qSbJbJCIMZOys9YlpddNBo4DfgT0A9pMfjXAnqs/+5rYGJaaclBvbSi1vnkZmVOZkuFYBFwBhCDGmQ3EHVRdJIOyG3r8Pe+XLI2rsugwWuWcWbhiwGgd05B4eYsjcnTigcg5U8IETt4xWJOWLkkOGSIecBppmluOFBlP5D2Ku3cbzRjYHmJB+DXrj09EkJQi9xXAhWmaX6AWnv3ouqIaNZGxS236muY2b2ftVVAvh8YpQNym/gPsAHVOvLtWw8H3jxiYdEjwEKBSAZK87KLlgM/oT73fm5vnT1y/hP0WP/dT8A5OiBrB6OcgsIpqAVxGlDz9pejTvwDUV06Y3VAbnvhPs/9ACuSexNwuZ3Ata3PJU8rjhO2/SlCxCZXrmXsql9bA/I7wLjOGpChHWvKAA9edsFzT19y21V+l5uTFs1+o3/VugvYbqmnX7r3XfbtgMP6IMTWTek2cGvF+PT/tFvhOqGS1LTTUInghwcfmrus31lXrup18jO0rhIlpYxoWrek+/rv+nWrmOV2Wt55wIk6KYh2sMvNyuyN6soZFXzobWBSTkGhnnvcDpKnFRuhPq/X4w5xnjHjI1IXz64Bhr9w/o01jRFRP3hCIw6LbKrn3HnTZbhlCaAQONs0zU6dc6Jdg3JuVuaITzLOK148KJ0Qr+fNK2Z9fhWqHy0DSCkcfnTPNfFdt5nDJmx7kTSMGyvGp3/TbgXr5IIpOD8BEgG/RKyrj+6zwhMSS2xd2ZEhvvrgyqR8CWSllZbUHqiyalpby83KTAYcOQWFnTIv/v408v2pRetjE8fHNdbJi996QjitAB+ecpFc0WuQcPu8nDVvpkzwNArge+Bk0zR3yK/f2bRrUAaYdNstxe9OvHyEYQUs2+HscXv+PRtzCgpl8rTi4cBsIDR90Y/1x82eGl0XFTfllfNuuFuPrm5/Jalpw4DngSN38vRPwJPAq3pQl6Zpv9UxBZ/0WhuXVO51uYWQNi6/H587BKcVYOLCH+hWVw1qQZazTNPUc/Fp3z5lAPqsKZvUbcNqbIfTcdjinyqAdXdef9WlqP7l0K6Va9edOLMwOszbsiq5at0DOiDvH2mlJb8AY1BN2aegEvDfiRrkNSattORlHZA1Tfs9vs+auOrwlaWTo1sakcLA5w4h1O/ldBWQVwJ3AyfpgLxFu9eUAS6/997nvjjhnKsAJsz8mIWph7MxqTsh3pa6q994LCbc02wDx+cUFH7b7oXRNE3T9qu/m/edXB8WPsXjco+Ma2ooDrECN+9tQqnOZr8EZYBh737xSVV819Nb74e3NHLBxy+QtGkDwG05BYW5+6UgmqZp2gFhmqbzUFpmsT3st6CcPK04DJUc4chea8tqT/nmg9jYhpo6ICenoPD5/VIITdM0TevA9ltQBkieVhwCxN+ef88GVHap9XtKVK5pmqZpncV+Dcqapmmapu1au4++1jRN0zRt7+igrGmapmkdhA7KmqZpmtZB6KCsaZqmaR2EDsqapmma1kHooKxpmqZpHYQOypqmaZrWQeigrGmapmkdhA7KmqZpmtZB6KCsaZqmaR2EDsqapmma1kHooKxpmqZpHYQOypqmaZrWQeigrGmapmkdhA7KmqZpmtZB6KCsaZqmaR2EDsqapmma1kHooKxpmqZpHYQOypqmaZrWQeigrGmapmkdhA7KmqZpmtZB6KCsaZqmaR2EDsqapmma1kHooKxpmqZpHYQOypqmaZrWQeigrGmapmkdhA7KmqZpmtZB6KCsaZqmaR2EDsqapmma1kHooKxpmqZpHcT/A7068+PX9AJ5AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 600x400 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig=plt.figure(dpi=100)\\n\",\n    \"plot_voltage_traces(output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1050x450 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Let's plot the hiddden layer spiking activity for some input stimuli\\n\",\n    \"\\n\",\n    \"nb_plt = 4\\n\",\n    \"gs = GridSpec(1,nb_plt)\\n\",\n    \"fig= plt.figure(figsize=(7,3),dpi=150)\\n\",\n    \"for i in range(nb_plt):\\n\",\n    \"    plt.subplot(gs[i])\\n\",\n    \"    plt.imshow(spk_rec[i].detach().cpu().numpy().T,cmap=plt.cm.gray_r, origin=\\\"lower\\\" )\\n\",\n    \"    if i==0:\\n\",\n    \"        plt.xlabel(\\\"Time\\\")\\n\",\n    \"        plt.ylabel(\\\"Units\\\")\\n\",\n    \"\\n\",\n    \"    sns.despine()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In conclusion, we see that already this simple spiking network solves the classification problem with ~85% accuracy, and there is plenty of room left for tweaking. However, the hidden layer activities do not look very biological. Although the network displays population sparseness in that only a subset of neurons are active at any given time, the individual neurons' firing rates are pathologically high. This pathology is not too surprising since we have not incentivized low activity levels in any way. We will create such an incentive to address this issue by activity regularization in one of the next tutorials.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a rel=\\\"license\\\" href=\\\"http://creativecommons.org/licenses/by/4.0/\\\"><img alt=\\\"Creative Commons License\\\" style=\\\"border-width:0\\\" src=\\\"https://i.creativecommons.org/l/by/4.0/88x31.png\\\" /></a><br />This work is licensed under a <a rel=\\\"license\\\" href=\\\"http://creativecommons.org/licenses/by/4.0/\\\">Creative Commons Attribution 4.0 International License</a>.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/SpyTorchTutorial3.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tutorial 3: Training a spiking neural network on a simple vision dataset\\n\",\n    \"\\n\",\n    \"Friedemann Zenke (https://fzenke.net)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> For more details on surrogate gradient learning, please see: \\n\",\n    \"> Neftci, E.O., Mostafa, H., and Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks.\\n\",\n    \"> https://arxiv.org/abs/1901.09948\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In Tutorial 2, we have seen how to train a simple multi-layer spiking neural network on the [Fashion MNIST dataset](https://github.com/zalandoresearch/fashion-mnist). However, the spiking activity in the hidden layer was not particularly plausible in a biological sense. Here we modify the network from this previous tutorial by adding activity regularizer, which encourages solutions with sparse spiking.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib.gridspec import GridSpec\\n\",\n    \"import seaborn as sns\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torchvision\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# The coarse network structure is dicated by the Fashion MNIST dataset. \\n\",\n    \"nb_inputs  = 28*28\\n\",\n    \"nb_hidden  = 100\\n\",\n    \"nb_outputs = 10\\n\",\n    \"\\n\",\n    \"time_step = 1e-3\\n\",\n    \"nb_steps  = 100\\n\",\n    \"\\n\",\n    \"batch_size = 256\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dtype = torch.float\\n\",\n    \"\\n\",\n    \"# Check whether a GPU is available\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    device = torch.device(\\\"cuda\\\")     \\n\",\n    \"else:\\n\",\n    \"    device = torch.device(\\\"cpu\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Here we load the Dataset\\n\",\n    \"root = os.path.expanduser(\\\"~/data/datasets/torch/fashion-mnist\\\")\\n\",\n    \"train_dataset = torchvision.datasets.FashionMNIST(root, train=True, transform=None, target_transform=None, download=True)\\n\",\n    \"test_dataset = torchvision.datasets.FashionMNIST(root, train=False, transform=None, target_transform=None, download=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Standardize data\\n\",\n    \"# x_train = torch.tensor(train_dataset.train_data, device=device, dtype=dtype)\\n\",\n    \"x_train = np.array(train_dataset.data, dtype=np.float)\\n\",\n    \"x_train = x_train.reshape(x_train.shape[0],-1)/255\\n\",\n    \"# x_test = torch.tensor(test_dataset.test_data, device=device, dtype=dtype)\\n\",\n    \"x_test = np.array(test_dataset.data, dtype=np.float)\\n\",\n    \"x_test = x_test.reshape(x_test.shape[0],-1)/255\\n\",\n    \"\\n\",\n    \"# y_train = torch.tensor(train_dataset.train_labels, device=device, dtype=dtype)\\n\",\n    \"# y_test  = torch.tensor(test_dataset.test_labels, device=device, dtype=dtype)\\n\",\n    \"y_train = np.array(train_dataset.targets, dtype=np.int)\\n\",\n    \"y_test  = np.array(test_dataset.targets, dtype=np.int)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(-0.5, 27.5, 27.5, -0.5)\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Here we plot one of the raw data points as an example\\n\",\n    \"data_id = 1\\n\",\n    \"plt.imshow(x_train[data_id].reshape(28,28), cmap=plt.cm.gray_r)\\n\",\n    \"plt.axis(\\\"off\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Since we are working with spiking neural networks, we ideally want to use a temporal code to make use of spike timing. To that end, we will use a spike latency code to feed spikes to our network.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def current2firing_time(x, tau=20, thr=0.2, tmax=1.0, epsilon=1e-7):\\n\",\n    \"    \\\"\\\"\\\" Computes first firing time latency for a current input x assuming the charge time of a current based LIF neuron.\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"    x -- The \\\"current\\\" values\\n\",\n    \"\\n\",\n    \"    Keyword args:\\n\",\n    \"    tau -- The membrane time constant of the LIF neuron to be charged\\n\",\n    \"    thr -- The firing threshold value \\n\",\n    \"    tmax -- The maximum time returned \\n\",\n    \"    epsilon -- A generic (small) epsilon > 0\\n\",\n    \"\\n\",\n    \"    Returns:\\n\",\n    \"    Time to first spike for each \\\"current\\\" x\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    idx = x<thr\\n\",\n    \"    x = np.clip(x,thr+epsilon,1e9)\\n\",\n    \"    T = tau*np.log(x/(x-thr))\\n\",\n    \"    T[idx] = tmax\\n\",\n    \"    return T\\n\",\n    \" \\n\",\n    \"\\n\",\n    \"def sparse_data_generator(X, y, batch_size, nb_steps, nb_units, shuffle=True ):\\n\",\n    \"    \\\"\\\"\\\" This generator takes datasets in analog format and generates spiking network input as sparse tensors. \\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"        X: The data ( sample x event x 2 ) the last dim holds (time,neuron) tuples\\n\",\n    \"        y: The labels\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    labels_ = np.array(y,dtype=np.int)\\n\",\n    \"    number_of_batches = len(X)//batch_size\\n\",\n    \"    sample_index = np.arange(len(X))\\n\",\n    \"\\n\",\n    \"    # compute discrete firing times\\n\",\n    \"    tau_eff = 20e-3/time_step\\n\",\n    \"    firing_times = np.array(current2firing_time(X, tau=tau_eff, tmax=nb_steps), dtype=np.int)\\n\",\n    \"    unit_numbers = np.arange(nb_units)\\n\",\n    \"\\n\",\n    \"    if shuffle:\\n\",\n    \"        np.random.shuffle(sample_index)\\n\",\n    \"\\n\",\n    \"    total_batch_count = 0\\n\",\n    \"    counter = 0\\n\",\n    \"    while counter<number_of_batches:\\n\",\n    \"        batch_index = sample_index[batch_size*counter:batch_size*(counter+1)]\\n\",\n    \"\\n\",\n    \"        coo = [ [] for i in range(3) ]\\n\",\n    \"        for bc,idx in enumerate(batch_index):\\n\",\n    \"            c = firing_times[idx]<nb_steps\\n\",\n    \"            times, units = firing_times[idx][c], unit_numbers[c]\\n\",\n    \"\\n\",\n    \"            batch = [bc for _ in range(len(times))]\\n\",\n    \"            coo[0].extend(batch)\\n\",\n    \"            coo[1].extend(times)\\n\",\n    \"            coo[2].extend(units)\\n\",\n    \"\\n\",\n    \"        i = torch.LongTensor(coo).to(device)\\n\",\n    \"        v = torch.FloatTensor(np.ones(len(coo[0]))).to(device)\\n\",\n    \"    \\n\",\n    \"        X_batch = torch.sparse.FloatTensor(i, v, torch.Size([batch_size,nb_steps,nb_units])).to(device)\\n\",\n    \"        y_batch = torch.tensor(labels_[batch_index],device=device)\\n\",\n    \"\\n\",\n    \"        yield X_batch.to(device=device), y_batch.to(device=device)\\n\",\n    \"\\n\",\n    \"        counter += 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setup of the spiking network model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tau_mem = 10e-3\\n\",\n    \"tau_syn = 5e-3\\n\",\n    \"\\n\",\n    \"alpha   = float(np.exp(-time_step/tau_syn))\\n\",\n    \"beta    = float(np.exp(-time_step/tau_mem))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"init done\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"weight_scale = 0.2\\n\",\n    \"\\n\",\n    \"w1 = torch.empty((nb_inputs, nb_hidden),  device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"torch.nn.init.normal_(w1, mean=0.0, std=weight_scale/np.sqrt(nb_inputs))\\n\",\n    \"\\n\",\n    \"w2 = torch.empty((nb_hidden, nb_outputs), device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"torch.nn.init.normal_(w2, mean=0.0, std=weight_scale/np.sqrt(nb_hidden))\\n\",\n    \"\\n\",\n    \"print(\\\"init done\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_voltage_traces(mem, spk=None, dim=(3,5), spike_height=5):\\n\",\n    \"    gs=GridSpec(*dim)\\n\",\n    \"    if spk is not None:\\n\",\n    \"        dat = 1.0*mem\\n\",\n    \"        dat[spk>0.0] = spike_height\\n\",\n    \"        dat = dat.detach().cpu().numpy()\\n\",\n    \"    else:\\n\",\n    \"        dat = mem.detach().cpu().numpy()\\n\",\n    \"    for i in range(np.prod(dim)):\\n\",\n    \"        if i==0: a0=ax=plt.subplot(gs[i])\\n\",\n    \"        else: ax=plt.subplot(gs[i],sharey=a0)\\n\",\n    \"        ax.plot(dat[i])\\n\",\n    \"        ax.axis(\\\"off\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the network\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class SurrGradSpike(torch.autograd.Function):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Here we implement our spiking nonlinearity which also implements \\n\",\n    \"    the surrogate gradient. By subclassing torch.autograd.Function, \\n\",\n    \"    we will be able to use all of PyTorch's autograd functionality.\\n\",\n    \"    Here we use the normalized negative part of a fast sigmoid \\n\",\n    \"    as this was done in Zenke & Ganguli (2018).\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    scale = 100.0 # controls steepness of surrogate gradient\\n\",\n    \"\\n\",\n    \"    @staticmethod\\n\",\n    \"    def forward(ctx, input):\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        In the forward pass we compute a step function of the input Tensor\\n\",\n    \"        and return it. ctx is a context object that we use to stash information which \\n\",\n    \"        we need to later backpropagate our error signals. To achieve this we use the \\n\",\n    \"        ctx.save_for_backward method.\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        ctx.save_for_backward(input)\\n\",\n    \"        out = torch.zeros_like(input)\\n\",\n    \"        out[input > 0] = 1.0\\n\",\n    \"        return out\\n\",\n    \"\\n\",\n    \"    @staticmethod\\n\",\n    \"    def backward(ctx, grad_output):\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        In the backward pass we receive a Tensor we need to compute the \\n\",\n    \"        surrogate gradient of the loss with respect to the input. \\n\",\n    \"        Here we use the normalized negative part of a fast sigmoid \\n\",\n    \"        as this was done in Zenke & Ganguli (2018).\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        input, = ctx.saved_tensors\\n\",\n    \"        grad_input = grad_output.clone()\\n\",\n    \"        grad = grad_input/(SurrGradSpike.scale*torch.abs(input)+1.0)**2\\n\",\n    \"        return grad\\n\",\n    \"    \\n\",\n    \"# here we overwrite our naive spike function by the \\\"SurrGradSpike\\\" nonlinearity which implements a surrogate gradient\\n\",\n    \"spike_fn  = SurrGradSpike.apply\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def run_snn(inputs):\\n\",\n    \"    h1 = torch.einsum(\\\"abc,cd->abd\\\", (inputs, w1))\\n\",\n    \"    syn = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\\n\",\n    \"    mem = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\\n\",\n    \"\\n\",\n    \"    mem_rec = []\\n\",\n    \"    spk_rec = []\\n\",\n    \"\\n\",\n    \"    # Compute hidden layer activity\\n\",\n    \"    for t in range(nb_steps):\\n\",\n    \"        mthr = mem-1.0\\n\",\n    \"        out = spike_fn(mthr)\\n\",\n    \"        rst = out.detach() # We do not want to backprop through the reset\\n\",\n    \"\\n\",\n    \"        new_syn = alpha*syn +h1[:,t]\\n\",\n    \"        new_mem = (beta*mem +syn)*(1.0-rst)\\n\",\n    \"\\n\",\n    \"        mem_rec.append(mem)\\n\",\n    \"        spk_rec.append(out)\\n\",\n    \"        \\n\",\n    \"        mem = new_mem\\n\",\n    \"        syn = new_syn\\n\",\n    \"\\n\",\n    \"    mem_rec = torch.stack(mem_rec,dim=1)\\n\",\n    \"    spk_rec = torch.stack(spk_rec,dim=1)\\n\",\n    \"\\n\",\n    \"    # Readout layer\\n\",\n    \"    h2= torch.einsum(\\\"abc,cd->abd\\\", (spk_rec, w2))\\n\",\n    \"    flt = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\\n\",\n    \"    out = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\\n\",\n    \"    out_rec = [out]\\n\",\n    \"    for t in range(nb_steps):\\n\",\n    \"        new_flt = alpha*flt +h2[:,t]\\n\",\n    \"        new_out = beta*out +flt\\n\",\n    \"\\n\",\n    \"        flt = new_flt\\n\",\n    \"        out = new_out\\n\",\n    \"\\n\",\n    \"        out_rec.append(out)\\n\",\n    \"\\n\",\n    \"    out_rec = torch.stack(out_rec,dim=1)\\n\",\n    \"    other_recs = [mem_rec, spk_rec]\\n\",\n    \"    return out_rec, other_recs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(x_data, y_data, lr=1e-3, nb_epochs=10):\\n\",\n    \"    params = [w1,w2]\\n\",\n    \"    optimizer = torch.optim.Adamax(params, lr=lr, betas=(0.9,0.999))\\n\",\n    \"\\n\",\n    \"    log_softmax_fn = nn.LogSoftmax(dim=1)\\n\",\n    \"    loss_fn = nn.NLLLoss()\\n\",\n    \"    \\n\",\n    \"    loss_hist = []\\n\",\n    \"    for e in range(nb_epochs):\\n\",\n    \"        local_loss = []\\n\",\n    \"        for x_local, y_local in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs):\\n\",\n    \"            output,recs = run_snn(x_local.to_dense())\\n\",\n    \"            _,spks=recs\\n\",\n    \"            m,_=torch.max(output,1)\\n\",\n    \"            log_p_y = log_softmax_fn(m)\\n\",\n    \"            \\n\",\n    \"            # Here we set up our regularizer loss\\n\",\n    \"            # The strength paramters here are merely a guess and there should be ample room for improvement by\\n\",\n    \"            # tuning these paramters.\\n\",\n    \"            reg_loss = 1e-5*torch.sum(spks) # L1 loss on total number of spikes\\n\",\n    \"            reg_loss += 1e-5*torch.mean(torch.sum(torch.sum(spks,dim=0),dim=0)**2) # L2 loss on spikes per neuron\\n\",\n    \"            \\n\",\n    \"            # Here we combine supervised loss and the regularizer\\n\",\n    \"            loss_val = loss_fn(log_p_y, y_local) + reg_loss\\n\",\n    \"\\n\",\n    \"            optimizer.zero_grad()\\n\",\n    \"            loss_val.backward()\\n\",\n    \"            optimizer.step()\\n\",\n    \"            local_loss.append(loss_val.item())\\n\",\n    \"        mean_loss = np.mean(local_loss)\\n\",\n    \"        print(\\\"Epoch %i: loss=%.5f\\\"%(e+1,mean_loss))\\n\",\n    \"        loss_hist.append(mean_loss)\\n\",\n    \"        \\n\",\n    \"    return loss_hist\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"def compute_classification_accuracy(x_data, y_data):\\n\",\n    \"    \\\"\\\"\\\" Computes classification accuracy on supplied data in batches. \\\"\\\"\\\"\\n\",\n    \"    accs = []\\n\",\n    \"    for x_local, y_local in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs, shuffle=False):\\n\",\n    \"        output,_ = run_snn(x_local.to_dense())\\n\",\n    \"        m,_= torch.max(output,1) # max over time\\n\",\n    \"        _,am=torch.max(m,1)      # argmax over output units\\n\",\n    \"        tmp = np.mean((y_local==am).detach().cpu().numpy()) # compare to labels\\n\",\n    \"        accs.append(tmp)\\n\",\n    \"    return np.mean(accs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1: loss=2.10319\\n\",\n      \"Epoch 2: loss=1.64978\\n\",\n      \"Epoch 3: loss=1.30702\\n\",\n      \"Epoch 4: loss=1.08484\\n\",\n      \"Epoch 5: loss=0.95575\\n\",\n      \"Epoch 6: loss=0.87375\\n\",\n      \"Epoch 7: loss=0.81585\\n\",\n      \"Epoch 8: loss=0.77363\\n\",\n      \"Epoch 9: loss=0.73897\\n\",\n      \"Epoch 10: loss=0.70913\\n\",\n      \"Epoch 11: loss=0.68416\\n\",\n      \"Epoch 12: loss=0.66362\\n\",\n      \"Epoch 13: loss=0.64633\\n\",\n      \"Epoch 14: loss=0.63024\\n\",\n      \"Epoch 15: loss=0.61729\\n\",\n      \"Epoch 16: loss=0.60596\\n\",\n      \"Epoch 17: loss=0.59438\\n\",\n      \"Epoch 18: loss=0.58355\\n\",\n      \"Epoch 19: loss=0.57630\\n\",\n      \"Epoch 20: loss=0.56730\\n\",\n      \"Epoch 21: loss=0.56177\\n\",\n      \"Epoch 22: loss=0.55338\\n\",\n      \"Epoch 23: loss=0.54661\\n\",\n      \"Epoch 24: loss=0.54120\\n\",\n      \"Epoch 25: loss=0.53651\\n\",\n      \"Epoch 26: loss=0.53226\\n\",\n      \"Epoch 27: loss=0.52634\\n\",\n      \"Epoch 28: loss=0.52089\\n\",\n      \"Epoch 29: loss=0.51909\\n\",\n      \"Epoch 30: loss=0.51457\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"loss_hist = train(x_train, y_train, lr=2e-4, nb_epochs=30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 495x300 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(3.3,2),dpi=150)\\n\",\n    \"plt.plot(loss_hist)\\n\",\n    \"plt.xlabel(\\\"Epoch\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"sns.despine()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training accuracy: 0.848\\n\",\n      \"Test accuracy: 0.827\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Training accuracy: %.3f\\\"%(compute_classification_accuracy(x_train,y_train)))\\n\",\n    \"print(\\\"Test accuracy: %.3f\\\"%(compute_classification_accuracy(x_test,y_test)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_mini_batch(x_data, y_data, shuffle=False):\\n\",\n    \"    for ret in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs, shuffle=shuffle):\\n\",\n    \"        return ret \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x_batch, y_batch = get_mini_batch(x_test, y_test)\\n\",\n    \"output, other_recordings = run_snn(x_batch.to_dense())\\n\",\n    \"mem_rec, spk_rec = other_recordings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 600x400 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig=plt.figure(dpi=100)\\n\",\n    \"plot_voltage_traces(output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1050x450 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Let's plot the hiddden layer spiking activity for some input stimuli\\n\",\n    \"\\n\",\n    \"nb_plt = 4\\n\",\n    \"gs = GridSpec(1,nb_plt)\\n\",\n    \"fig= plt.figure(figsize=(7,3),dpi=150)\\n\",\n    \"for i in range(nb_plt):\\n\",\n    \"    plt.subplot(gs[i])\\n\",\n    \"    plt.imshow(spk_rec[i].detach().cpu().numpy().T,cmap=plt.cm.gray_r, origin=\\\"lower\\\" )\\n\",\n    \"    if i==0:\\n\",\n    \"        plt.xlabel(\\\"Time\\\")\\n\",\n    \"        plt.ylabel(\\\"Units\\\")\\n\",\n    \"\\n\",\n    \"    sns.despine()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Compared to the hidden layer activity in our previous Tutorial 2, we can now appreciate that spiking in the hidden layer is much sparser.\\n\",\n    \"\\n\",\n    \"In the next tutorial notebook, we will apply the same training paradigm to the Heidelberg Digits, a realistic speech dataset generated from spoken digits processed through a plausible cochlea model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a rel=\\\"license\\\" href=\\\"http://creativecommons.org/licenses/by/4.0/\\\"><img alt=\\\"Creative Commons License\\\" style=\\\"border-width:0\\\" src=\\\"https://i.creativecommons.org/l/by/4.0/88x31.png\\\" /></a><br />This work is licensed under a <a rel=\\\"license\\\" href=\\\"http://creativecommons.org/licenses/by/4.0/\\\">Creative Commons Attribution 4.0 International License</a>.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/SpyTorchTutorial4.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tutorial 4: Training a spiking neural network on a spiking dataset (Spiking Heidelberg Digits)\\n\",\n    \"\\n\",\n    \"Manu Srinath Halvagal (https://zenkelab.org/team/) and Friedemann Zenke (https://fzenke.net)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For more details on surrogate gradient learning, please see: \\n\",\n    \"\\n\",\n    \"> Neftci, E.O., Mostafa, H., and Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks. IEEE Signal Process Mag 36, 51–63.\\n\",\n    \"> https://ieeexplore.ieee.org/document/8891809 and https://arxiv.org/abs/1901.09948\\n\",\n    \"\\n\",\n    \"> Cramer, B., Stradmann, Y., Schemmel, J., and Zenke, F. (2020). The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks. IEEE Transactions on Neural Networks and Learning Systems 1–14. \\n\",\n    \"> https://ieeexplore.ieee.org/document/9311226 and https://arxiv.org/abs/1910.07407\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In Tutorials 2 and 3, we have seen how to train a multi-layer spiking neural network on the [Fashion MNIST dataset](https://github.com/zalandoresearch/fashion-mnist) along with an activity regularizer. However, the spiking activity input to the network was generated using a simple time-to-first-spike code. Here we apply the model to train a spiking network to learn the Spiking Heidelberg Digits dataset (https://compneuro.net/posts/2019-spiking-heidelberg-digits/). This dataset uses a more sophisticated cochlear model to generate the spike data corresponding to audio recordings of spoken digits (examples below).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![Image of Yaktocat](https://compneuro.net/img/hdspikes_digits_samples.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import h5py\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib.gridspec import GridSpec\\n\",\n    \"import seaborn as sns\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torchvision\\n\",\n    \"from torch.utils import data\\n\",\n    \"\\n\",\n    \"from utils import get_shd_dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# The coarse network structure and the time steps are dicated by the SHD dataset. \\n\",\n    \"nb_inputs  = 700\\n\",\n    \"nb_hidden  = 200\\n\",\n    \"nb_outputs = 20\\n\",\n    \"\\n\",\n    \"time_step = 1e-3\\n\",\n    \"nb_steps = 100\\n\",\n    \"max_time = 1.4\\n\",\n    \"\\n\",\n    \"batch_size = 256\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dtype = torch.float\\n\",\n    \"\\n\",\n    \"# Check whether a GPU is available\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    device = torch.device(\\\"cuda\\\")     \\n\",\n    \"else:\\n\",\n    \"    device = torch.device(\\\"cpu\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setup of the spiking dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"File shd_train.h5.gz decompressed to:\\n\",\n      \"/home/zenkfrie/data/hdspikes/shd_train.h5.gz\\n\",\n      \"File shd_test.h5.gz decompressed to:\\n\",\n      \"/home/zenkfrie/data/hdspikes/shd_test.h5.gz\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Here we load the Dataset\\n\",\n    \"cache_dir = os.path.expanduser(\\\"~/data\\\")\\n\",\n    \"cache_subdir = \\\"hdspikes\\\"\\n\",\n    \"get_shd_dataset(cache_dir, cache_subdir)\\n\",\n    \"\\n\",\n    \"train_file = h5py.File(os.path.join(cache_dir, cache_subdir, 'shd_train.h5'), 'r')\\n\",\n    \"test_file = h5py.File(os.path.join(cache_dir, cache_subdir, 'shd_test.h5'), 'r')\\n\",\n    \"\\n\",\n    \"x_train = train_file['spikes']\\n\",\n    \"y_train = train_file['labels']\\n\",\n    \"x_test = test_file['spikes']\\n\",\n    \"y_test = test_file['labels']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The code for learning the SHD dataset is nearly identical to what we have seen for the FashionMNIST dataset in the last two tutorials. An important difference is that, now, we have the input data already in the form of spikes. This is reflected in the sparse_data_generator below. \\n\",\n    \"\\n\",\n    \"In order to use the data for learning with our spiking network, we also need to discretize the spike times into n_steps bins. Note the additional max_time argument (~1.4 for SHD) that forms the upper limit of the bins.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def sparse_data_generator_from_hdf5_spikes(X, y, batch_size, nb_steps, nb_units, max_time, shuffle=True):\\n\",\n    \"    \\\"\\\"\\\" This generator takes a spike dataset and generates spiking network input as sparse tensors. \\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"        X: The data ( sample x event x 2 ) the last dim holds (time,neuron) tuples\\n\",\n    \"        y: The labels\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    labels_ = np.array(y,dtype=np.int)\\n\",\n    \"    number_of_batches = len(labels_)//batch_size\\n\",\n    \"    sample_index = np.arange(len(labels_))\\n\",\n    \"\\n\",\n    \"    # compute discrete firing times\\n\",\n    \"    firing_times = X['times']\\n\",\n    \"    units_fired = X['units']\\n\",\n    \"    \\n\",\n    \"    time_bins = np.linspace(0, max_time, num=nb_steps)\\n\",\n    \"\\n\",\n    \"    if shuffle:\\n\",\n    \"        np.random.shuffle(sample_index)\\n\",\n    \"\\n\",\n    \"    total_batch_count = 0\\n\",\n    \"    counter = 0\\n\",\n    \"    while counter<number_of_batches:\\n\",\n    \"        batch_index = sample_index[batch_size*counter:batch_size*(counter+1)]\\n\",\n    \"\\n\",\n    \"        coo = [ [] for i in range(3) ]\\n\",\n    \"        for bc,idx in enumerate(batch_index):\\n\",\n    \"            times = np.digitize(firing_times[idx], time_bins)\\n\",\n    \"            units = units_fired[idx]\\n\",\n    \"            batch = [bc for _ in range(len(times))]\\n\",\n    \"            \\n\",\n    \"            coo[0].extend(batch)\\n\",\n    \"            coo[1].extend(times)\\n\",\n    \"            coo[2].extend(units)\\n\",\n    \"\\n\",\n    \"        i = torch.LongTensor(coo).to(device)\\n\",\n    \"        v = torch.FloatTensor(np.ones(len(coo[0]))).to(device)\\n\",\n    \"    \\n\",\n    \"        X_batch = torch.sparse.FloatTensor(i, v, torch.Size([batch_size,nb_steps,nb_units])).to(device)\\n\",\n    \"        y_batch = torch.tensor(labels_[batch_index],device=device)\\n\",\n    \"\\n\",\n    \"        yield X_batch.to(device=device), y_batch.to(device=device)\\n\",\n    \"\\n\",\n    \"        counter += 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setup of the spiking network model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tau_mem = 10e-3\\n\",\n    \"tau_syn = 5e-3\\n\",\n    \"\\n\",\n    \"alpha   = float(np.exp(-time_step/tau_syn))\\n\",\n    \"beta    = float(np.exp(-time_step/tau_mem))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's also now include recurrent weights in the hidden layer. This significantly improves performance on the SHD dataset .\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"init done\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"weight_scale = 0.2\\n\",\n    \"\\n\",\n    \"w1 = torch.empty((nb_inputs, nb_hidden),  device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"torch.nn.init.normal_(w1, mean=0.0, std=weight_scale/np.sqrt(nb_inputs))\\n\",\n    \"\\n\",\n    \"w2 = torch.empty((nb_hidden, nb_outputs), device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"torch.nn.init.normal_(w2, mean=0.0, std=weight_scale/np.sqrt(nb_hidden))\\n\",\n    \"\\n\",\n    \"v1 = torch.empty((nb_hidden, nb_hidden), device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"torch.nn.init.normal_(v1, mean=0.0, std=weight_scale/np.sqrt(nb_hidden))\\n\",\n    \"\\n\",\n    \"print(\\\"init done\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_voltage_traces(mem, spk=None, dim=(3,5), spike_height=5):\\n\",\n    \"    gs=GridSpec(*dim)\\n\",\n    \"    if spk is not None:\\n\",\n    \"        dat = 1.0*mem\\n\",\n    \"        dat[spk>0.0] = spike_height\\n\",\n    \"        dat = dat.detach().cpu().numpy()\\n\",\n    \"    else:\\n\",\n    \"        dat = mem.detach().cpu().numpy()\\n\",\n    \"    for i in range(np.prod(dim)):\\n\",\n    \"        if i==0: a0=ax=plt.subplot(gs[i])\\n\",\n    \"        else: ax=plt.subplot(gs[i],sharey=a0)\\n\",\n    \"        ax.plot(dat[i])\\n\",\n    \"        ax.axis(\\\"off\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from IPython.display import clear_output\\n\",\n    \"\\n\",\n    \"def live_plot(loss):\\n\",\n    \"    if len(loss) == 1:\\n\",\n    \"        return\\n\",\n    \"    clear_output(wait=True)\\n\",\n    \"    ax = plt.figure(figsize=(3,2), dpi=150).gca()\\n\",\n    \"    ax.plot(range(1, len(loss) + 1), loss)\\n\",\n    \"    ax.set_xlabel(\\\"Epoch\\\")\\n\",\n    \"    ax.set_ylabel(\\\"Loss\\\")\\n\",\n    \"    ax.xaxis.get_major_locator().set_params(integer=True)\\n\",\n    \"    sns.despine()\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the network\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class SurrGradSpike(torch.autograd.Function):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Here we implement our spiking nonlinearity which also implements \\n\",\n    \"    the surrogate gradient. By subclassing torch.autograd.Function, \\n\",\n    \"    we will be able to use all of PyTorch's autograd functionality.\\n\",\n    \"    Here we use the normalized negative part of a fast sigmoid \\n\",\n    \"    as this was done in Zenke & Ganguli (2018).\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    scale = 100.0 # controls steepness of surrogate gradient\\n\",\n    \"\\n\",\n    \"    @staticmethod\\n\",\n    \"    def forward(ctx, input):\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        In the forward pass we compute a step function of the input Tensor\\n\",\n    \"        and return it. ctx is a context object that we use to stash information which \\n\",\n    \"        we need to later backpropagate our error signals. To achieve this we use the \\n\",\n    \"        ctx.save_for_backward method.\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        ctx.save_for_backward(input)\\n\",\n    \"        out = torch.zeros_like(input)\\n\",\n    \"        out[input > 0] = 1.0\\n\",\n    \"        return out\\n\",\n    \"\\n\",\n    \"    @staticmethod\\n\",\n    \"    def backward(ctx, grad_output):\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        In the backward pass we receive a Tensor we need to compute the \\n\",\n    \"        surrogate gradient of the loss with respect to the input. \\n\",\n    \"        Here we use the normalized negative part of a fast sigmoid \\n\",\n    \"        as this was done in Zenke & Ganguli (2018).\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        input, = ctx.saved_tensors\\n\",\n    \"        grad_input = grad_output.clone()\\n\",\n    \"        grad = grad_input/(SurrGradSpike.scale*torch.abs(input)+1.0)**2\\n\",\n    \"        return grad\\n\",\n    \"    \\n\",\n    \"# here we overwrite our naive spike function by the \\\"SurrGradSpike\\\" nonlinearity which implements a surrogate gradient\\n\",\n    \"spike_fn  = SurrGradSpike.apply\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"run_snn is also changed now in order to include the recurrent input in the hidden layer computation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def run_snn(inputs):\\n\",\n    \"    syn = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\\n\",\n    \"    mem = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\\n\",\n    \"\\n\",\n    \"    mem_rec = []\\n\",\n    \"    spk_rec = []\\n\",\n    \"\\n\",\n    \"    # Compute hidden layer activity\\n\",\n    \"    out = torch.zeros((batch_size, nb_hidden), device=device, dtype=dtype)\\n\",\n    \"    h1_from_input = torch.einsum(\\\"abc,cd->abd\\\", (inputs, w1))\\n\",\n    \"    for t in range(nb_steps):\\n\",\n    \"        h1 = h1_from_input[:,t] + torch.einsum(\\\"ab,bc->ac\\\", (out, v1))\\n\",\n    \"        mthr = mem-1.0\\n\",\n    \"        out = spike_fn(mthr)\\n\",\n    \"        rst = out.detach() # We do not want to backprop through the reset\\n\",\n    \"\\n\",\n    \"        new_syn = alpha*syn +h1\\n\",\n    \"        new_mem =(beta*mem +syn)*(1.0-rst)\\n\",\n    \"\\n\",\n    \"        mem_rec.append(mem)\\n\",\n    \"        spk_rec.append(out)\\n\",\n    \"        \\n\",\n    \"        mem = new_mem\\n\",\n    \"        syn = new_syn\\n\",\n    \"\\n\",\n    \"    mem_rec = torch.stack(mem_rec,dim=1)\\n\",\n    \"    spk_rec = torch.stack(spk_rec,dim=1)\\n\",\n    \"\\n\",\n    \"    # Readout layer\\n\",\n    \"    h2= torch.einsum(\\\"abc,cd->abd\\\", (spk_rec, w2))\\n\",\n    \"    flt = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\\n\",\n    \"    out = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\\n\",\n    \"    out_rec = [out]\\n\",\n    \"    for t in range(nb_steps):\\n\",\n    \"        new_flt = alpha*flt +h2[:,t]\\n\",\n    \"        new_out = beta*out +flt\\n\",\n    \"\\n\",\n    \"        flt = new_flt\\n\",\n    \"        out = new_out\\n\",\n    \"\\n\",\n    \"        out_rec.append(out)\\n\",\n    \"\\n\",\n    \"    out_rec = torch.stack(out_rec,dim=1)\\n\",\n    \"    other_recs = [mem_rec, spk_rec]\\n\",\n    \"    return out_rec, other_recs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(x_data, y_data, lr=1e-3, nb_epochs=10):\\n\",\n    \"    \\n\",\n    \"    params = [w1,w2,v1]\\n\",\n    \"    optimizer = torch.optim.Adamax(params, lr=lr, betas=(0.9,0.999))\\n\",\n    \"\\n\",\n    \"    log_softmax_fn = nn.LogSoftmax(dim=1)\\n\",\n    \"    loss_fn = nn.NLLLoss()\\n\",\n    \"    \\n\",\n    \"    loss_hist = []\\n\",\n    \"    for e in range(nb_epochs):\\n\",\n    \"        local_loss = []\\n\",\n    \"        for x_local, y_local in sparse_data_generator_from_hdf5_spikes(x_data, y_data, batch_size, nb_steps, nb_inputs, max_time):\\n\",\n    \"            output,recs = run_snn(x_local.to_dense())\\n\",\n    \"            _,spks=recs\\n\",\n    \"            m,_=torch.max(output,1)\\n\",\n    \"            log_p_y = log_softmax_fn(m)\\n\",\n    \"            \\n\",\n    \"            # Here we set up our regularizer loss\\n\",\n    \"            # The strength paramters here are merely a guess and there should be ample room for improvement by\\n\",\n    \"            # tuning these paramters.\\n\",\n    \"            reg_loss = 2e-6*torch.sum(spks) # L1 loss on total number of spikes\\n\",\n    \"            reg_loss += 2e-6*torch.mean(torch.sum(torch.sum(spks,dim=0),dim=0)**2) # L2 loss on spikes per neuron\\n\",\n    \"            \\n\",\n    \"            # Here we combine supervised loss and the regularizer\\n\",\n    \"            loss_val = loss_fn(log_p_y, y_local) + reg_loss\\n\",\n    \"\\n\",\n    \"            optimizer.zero_grad()\\n\",\n    \"            loss_val.backward()\\n\",\n    \"            optimizer.step()\\n\",\n    \"            local_loss.append(loss_val.item())\\n\",\n    \"        mean_loss = np.mean(local_loss)\\n\",\n    \"        loss_hist.append(mean_loss)\\n\",\n    \"        live_plot(loss_hist)\\n\",\n    \"        print(\\\"Epoch %i: loss=%.5f\\\"%(e+1,mean_loss))\\n\",\n    \"        \\n\",\n    \"    return loss_hist\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"def compute_classification_accuracy(x_data, y_data):\\n\",\n    \"    \\\"\\\"\\\" Computes classification accuracy on supplied data in batches. \\\"\\\"\\\"\\n\",\n    \"    accs = []\\n\",\n    \"    for x_local, y_local in sparse_data_generator_from_hdf5_spikes(x_data, y_data, batch_size, nb_steps, nb_inputs, max_time, shuffle=False):\\n\",\n    \"        output,_ = run_snn(x_local.to_dense())\\n\",\n    \"        m,_= torch.max(output,1) # max over time\\n\",\n    \"        _,am=torch.max(m,1)      # argmax over output units\\n\",\n    \"        tmp = np.mean((y_local==am).detach().cpu().numpy()) # compare to labels\\n\",\n    \"        accs.append(tmp)\\n\",\n    \"    return np.mean(accs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"nb_epochs = 200\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"WARNING: Training for a large number of epochs could take a significant amount of time. Reduce the nb_epochs parameter as necessary.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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pmCw3NumxQRKIviD22fwKygeucc+9wzt3vnFvlnFvtnHvAOXcd3mDIuXgDJktAPnLeVMoLcwE43N7Fzx7fFHJFIiLBCyLYzgGedc79YaAF/PeWAecG8P3iKy/M5e/Pnxab/tXybWzQRdsiEnFBBFs58PpxLPc6cbe2kWDccPZkJo4sArz7td30wFp0iYeIRFkQwbYH75zasSzwl5UAFeRm87Ur58Smn91cp5uRikikBRFsjwAzzew7/sj+RzHPzXgDJT8cwPdLHxfPrubCmaNi09968FVer2sZZA0RkfSV8JFHzGw88BIwEu9w42+Bbf7bk/A6jkwG6oA3OedqE1pAgqTryCMD2dvYxiX/8jSHWr3r2c6aWslvPnIGZhqRRERSRmqOPOIH1UXAWrwguxH4N//xJWAKsAa4KFVDLYpGlxXw7WvmxaaXb6njgVW7QqxIRCQYOUF8qHPuFbxb11yA1/OxdxiMXcAzzrkng/heGdyVJ4/lvpd28pd1+wC4eck6LpxVTVlBbsiViYgkTiDB1ssPsCf7e8/MPgiMd859M8ga5GjfeOtcntl4gPauHvY3tfOTRzdw01Vzwy5LRCRhwhwE+SPAN0L8/ow0YWQRn7pwemz618u3sXbXoRArEhFJLI3un4E+ev5UplQduSHpV+59hY6unpCrEhFJDAVbBsrPyeaf4w4/rqo9xDcfjEbvTxERBVuGOu+kUbz79Amx6buee53bntkSYkUiIomhYMtgN101l1PGHxnV7OYl6/j5U5tDrEhEZPgUbBmsIDebn79vIRNGFsbmfe9Pr/Hk+n0hViUiMjwKtgxXU17I3R89i8mVRbF5X7pnte7dJiJpa9jBZmbdQ3kAixJQvyTA2IpCfvG+08jL9jaHfU3tfPW+V3QXABFJS4nYY7NhPCRFzBxTypcumxmbfuiVPdz38s4QKxIRGZphB5tzLmsYjzeM/i/h+eDZUzhramVs+uv3rWV7XXOIFYmInDidY5OYrCzjR+88hdJ8b6S1pvYubrjzBQ61dIZcmYjI8VOwyVHGVRTy3Wvnx6a37G/mY3et0MgkIpI2FGzyBleePJbPXXxSbPq5LfV85Q/qTCIi6UHBJv36hzdP55pTx8Wm71lZy7eXrFO4iUjKU7BJv8yM7107n0WTR8bm3bZ0Kzc9sFbhJiIpTcEmA8rPyeYX71vI3LFlsXm/Wr6d25duDbEqEZHBKdhkUCOK8/jNh8/klAkVsXnfeWgdT7ymYbdEJDUp2OSYyotyuf0DpzGuwhtTssfBP/zvS2zc2xRyZSIib6Rgk+NSVZLPbR84jaI875r6pvYuPvirF1izU3ffFpHUomCT4za7poxb3rUA8wdD21HfytW3LtN93EQkpSjY5IRcMncMX718dmy6q8dx85J1/OiR9eotKSIpQcEmJ+zD507lNx8+I3bODeDfntjEzx7fFGJVIiIeBZsMyeLpVfzhE4s5aXRJbN5P/ryBe1bWhliViIiCTYahuqyA//voWUwdVRybd+M9q7j1iU06LCkioVGwybCMLM7jzusXUVWSB4Bz8MNH1vOVP6yhp0fhJiLJp2CTYZtYWcT/ffRMJlUWxeb97/Ov87nfvkxze1eIlYlIJlKwSUJMry7lvk+czcJJI2Lz7n95F5fe8jTPbakLsTIRyTRpG2xmttDMvmxm95pZrZk5M9OxrxCNKM7j1x9cxOJpR+7CXdvQyt/e9ld+8dRmnXcTkaRI22ADvgZ8F7gGGHeMZSVJivNzuOOG0/mHi6aTk+Vdyd3d4/jun17jY/+9ksY23Y1bRIKVzsG2HPgWcBVQA7SHW470ys/J5vOXzOT3H1981LVuj766l6t+tpR1uxtDrE5Eos6icnjIzNqAfOecJejz1s6ZM2fO2rVrE/FxGauhuYPP3v0yT23YH5tXkJvFzVfP5x0Lx4dYmYikoIT8/U7nPTZJAyOK87jj+tP53MUnxcaYbOvs4Yu/W8U/3buats7ucAsUkchRsEngsrKMz1w8gztvWERFUW5s/v8+v4Orb13Gyu31IVYnIlGjYJOkOf+kUTz46XM4ZXx5bN5re5q49j+W8+7/XM6+xrYQqxORqMj4c2xmNtBJtGlz5szJ1zm2xGvv6uYHD6/n9qVbj5o/paqYu/oMriwiGUXn2CQ95edk87Ur5/A/Hz6DRVNGxuZvPdDM3/zkKb770Dr2N6mTq4gMTcbvsQ3yeeoVmSS3PrGJHz6y/qh5BblZfPicqXzub04iOysh/0lFJPVpj02i4ZMXTuf7185nRFzHkrbOHv7tiU189NcrOKzxJkXkBCjYJCW86/SJLP3Hi/jK5bNidwoAeOy1fVx2y9NHXQcnIjIYBZukjOL8HD563jSeuvFCLpkzOja/tqGVD/zyeT5/98s0NHeEWKGIpAMFm6Sc4vwc/uPvFvKZN8+IjTcJcO9LO7n4J0/x+5W1tHTo8KSI9C9tO4+Y2RV4AyH3WoR34vGvcfO+5ZxbMsTPV+eRFLBudyNf/v1qVtUeOmp+TpZxwcxR3HD2FBZPq8RMHUxEIiAxnf/SONiuB+44xmI3OOfuHOLnK9hSRHeP445lW/nxoxto7WcIrsvmjuH77ziZ8sLcftYWkTSS2cEWNAVb6tlR38IPHlnPk6/to6lPT8lxFYV84ZKTeNuCcbo8QCR9KdiCpGBLXV3dPSzbXMetj2/i+W1HjzM5pqyAt79pHNcuHM+0USUhVSgiQ6RgC5KCLfX19DhufWITP31sI109b9yOL549mu9fO5/KkvwQqhORIVCwBUnBlj62HWjmX/6ygSWrd78h4EaX5fORc6dy1SljqS4rCKlCETlOCrYgKdjSz4HD7dz/8i5+t2IHr+1pOuq9LIOzp1fxqQunc8bUypAqFJFjULAFScGWvpxz3PnsNr7z0Do6u9+4fV+/eDKfuGCa9uBEUo+CLUgKtvS3va6Z362o5Q8v7WTnwdaj3svJMi6dN4b3nzmJRVNG6jo4kdSgYAuSgi06enocz22t4xv3r2XjvsNveP+8k0bx5ctmMbumVAEnEi4FW5AUbNHT1tnNL5dt5a7l29l16I13655UWcRVp4zl2jeNZ3JVcQgVimQ8BVuQFGzR1dXdw2Ov7eP2pVt5fmt9v8ssmjySDyyezFvmjSFLF3yLJIuCLUgKtuhzzvHwmj3c+uQm1uxs7HeZorxsCnOzGVtRyOmTR/LeMyYwvbo0yZWKZAwFW5AUbJll96FW/rhqF79bUdvvebheWQZXnDyWK+aP4YKZ1RTkZiexSpHIU7AFScGWmZxzrKo9xH89vYUlr+wedNnK4jxuOHsyN5w9heL8nCRVKBJpCrYgKdjk9boWttY1U5SXzau7GvnVs9vYcqD5DcuNKs3nA2dN4oyplSycOELn5ESGTsEWJAWb9NXV3cMja/fy6Kt7eGTtHto6e96wzJyaMt552nhqKgo5dWIF1aW6CFzkBCjYgqRgk8HUN3dw+9It3PbMVtq73hhwvd40sYKPXzCdi2dX6xo5kWNTsAVJwSbHY9fBVu5+YQd/3VrHc1v6v3Sg18jiPE4aXcI506t4/+LJlBXoxqgifSjYgqRgkxO1uvYgdy7bRu3BVmrrW/q9CLxXRVEu1y0cz1vm17BgfIXOy4l4FGxBUrDJcDjnWF17iFv+soEn1u8fdNma8gJOGV/BlFHFXLdwPFN1g1TJXAq2ICnYJFEOtnRQ39zBnsY2nt1Uxx3LttLc0d3vsmZwwUmjmDO2jGc311GYm823rp6nu4FLplCwBUnBJkGpb+5gyepdPPTKHv66tY5+bv59FDO4Yn4NM6pLuebUcUysLEpOoSLJp2ALkoJNkuHA4Xae3VzH9gPN3L9qF5sGGfUEvNvtXHFyDRfOrOak0aVMqizSxeESJQq2ICnYJNl6ehzPbq7jL+v2UtvQwra6lmMGHXgXiJ89rZLL59cwb1w5NeUFurRA0pWCLUgKNkkFz246wK+XbwfgpR0N7G1sP+Y6lcV5nDZ5BKdPHskpEyqYUlVMTpZRkJutsS0l1SnYgqRgk1TT0dXDn1/dy5Pr9/HyjoNsr2+hY5CLw/vKMphdU8Y5M6q4fF4NJ48v156dpBoFW5AUbJLqenocexrbWLXjIH9cvYuXXj/I7kGunetr/IhCzp1RxcSRxSyeVqmgk1SgYAuSgk3SUUNzBy++3sAL2xpYub2e1/Y00dTWdVzrlhXkUFaYy/xx5bxlfg2nTqigsa2TvY1tzBtbTnWZxr2UwCnYgqRgkyhwztHY1oWZN/zXs5vq+NOa3azY3sCJ/q+/cNII/vaMiVxxcg35OTpXJ4FQsAVJwSZRtrexjcfW7WPTvsO8svPgCQVdZXEel84bQ2lBDpNGFnPJ3NFUleQHW7BkCgVbkBRskkkOHG5nR30L+5raeWTtHp7fWk9tQyu52UZ5YR4HDh+7N2Z2lpGdZUwYUcjsmjJm15Qxp6aMcSMKqW1oYWxFIbPGlCXh10gaU7AFScEmma6lo4ucrCxys401Oxu567nt3PfyzkFv03MsF82q5m0LxlJTXsibJlaQk52VwIolAhRsQVKwibxRQ3MHv3+xlvV7mnDA0o0H2NN4/D0x41WV5LNgQgVFedkU5mYzpryAUydWcOqEEZQX6ZY+GUrBFiQFm8ix9fQ49ja10dXt6HGO1s5uNu49zLrdjf6jib1NbSfcUWXaqGIWTBhBXo7R1e1YNGUki6dXMaasgGzd4ifKFGxBUrCJJIZzDufgtyt2cO9LO2lq62LTviY6u0/8b09edhZzx5VxUnUpDkd3jzd+5ujyAmZUl3DG1JGMKsnX9XjpS8EWJAWbSHAamjt4asN+Dhxup62zm8Pt3Wzc28SLrzfQ0NI5rM/Oz8miqiSfypI8ZlSXctLoErp6HFOqirloVrWGFUttCrYgKdhEks85x/a6Fl7a0cCanY3kZBtNbV08+dq+Qe9IfrzKC3P5mzmjmVFdQn1LB6NK8pleXcL06hLGlhfqTubhU7AFScEmklpaOrrYvK+ZFdvr2dfUTrYZWVlGe1c3OxtaWbm94YSGFOurKC+b6dUlnDZpJAW5WexoaGX+uDLevWgiZQW5rNpxkKc27GdnQys1FQW894yJVJdqNJYEU7AFScEmkl6cc9Q1d1B3uIO6w+3sOtTG6tqD7DrYRmd3D89vrae1s/87lw+mIDeLorwc6ps7jppflJfN+86cxDsWjic/J5vOnh66exyd3T20dXbT3tXD9FElGorsxCjYgqRgE4mWprZOnli/n6fW7+dQaycji3PZdbCNTfsOD/mSheNRU17AyePLyc4yenrgrGmV/uDTRbqO740UbEFSsIlkjqa2Tjbvb+aV2oP8dWs9Pc5hZjyzYT+NcYNInzqxgokji3h6w/5hd3LJzTYmjixienUJM0eXkpeTxf6mdjbuO0xudhZTqoqZOqrYfy6hpqwgE84BKtiCpGATkbbObl7d3cj2umamVJVwin9rn6a2Tu5+YQd3PbedbXUtgBdUOVlZ5GR7N3V1juMaiux45edkMbaikNxsY3RZAVOqimlu72ZkcS5vWzCOSZVF5GRlUZiXTXtXNy3t3VQU5abbpQ8KtiAp2ETkeHR195CdZf0GyN7GNl7ecZB1uxvJzc7iUGsnj63by5YDzSd80frxGlWaz8GWDjq7HbPGlHLO9Cryc7OYNaaMUydW4Bx0dHvDolUW51Fe6IXflv3eIdlZY8oYWZwXTHHHpmALkoJNRILS1tnNtrpmtuxvZsPeJjbvb8aA0oIcZlR7191t3t/M1gOH2Xqgmb2Nidvz62tEUS652VnsazryHZMqi1gwoYIFEyqoLMmnub0LA0oKcphcWcyY8gIOHG5n6cYDFOfn8ObZ1YnqIapgC5KCTURSxeH2LrYdaGZ/Uzud3T1sr2th58FWcrKMF7bVs6r2UKj1mcHY8kKmVBVz1rRKPnnh9CF/VCLqyUnEh4iISHBK8nOYN658wPcPtnTQ46C1s5vtdc2UF+aSnWU8uGo3exvbaOvq4bktdez398pys40eB909R+/YlOTncLj9+O64Hs852HmwlZ0HWynOD39kFwWbiEiaqyg6ck5sXEVh7HX8/e+cc3R2O3KzvfOBvdf9Pb+1nu11LZw8vpzF0yqpbWjl5R0HeXnHQV6pPUR7VzfF+Tk4B/XNHWyvb6at0ztHd/L4cuoOd7DzYGvseyZXFifhFw9OhyIHoEORIiJv5JyjpaObHucoLcilp8exvb6FbXXNbD/QzLxx5Zw2eeRQP16HIkVEJLnMjOL8I9GRlWVMqfKut2NmiIXF0WXvIiISKQo2ERGJFAWbiIhEioJNREQiRcEmIiKRomATEZFI0XVsAzCzxvz8/NJp06aFXYqISEZ49dVX/+icu2q4n6NgG4CZ7QGKgB0nuGpvEm5ObEUZQ+03PGq/oVPbDU8i2m+zgi0FmdlaAOfc3LBrSUdqv+FR+w2d2m54Uqn9dI5NREQiRcEmIiKRomATEZFIUbCJiEikKNhERCRS1CtSREQiRXtsIiISKQo2ERGJFAWbiIhEioJNREQiRcEmIiKRomATEZFIUbCJiEikKNgSxMwKzeybZrbBzNrMbJeZ/dLMxoVdWyowsyfNzA3yuGyA9a43s+fN7LCZ1ZvZQ2a2ONn1J4OZLTSzL5vZvWZW29s2x7HeCbeRmZ3tL1fvr/e8mb0/cb8m+U60/czspmNsk98bZN1ItZ+ZFZnZ1WZ2u5mt9/+GNZvZKjP7upmVDLJu6m1/zjk9hvkACoDlgAN2AXcDf/Wn9wFTw64x7AfwpN8e9wB39vOY3886t/jrtAD3AQ8DnUAXcHXYvymANrrP/71HPY6xzgm3EXCt/36P/9/lHqDB/5wfhd0OyWo/4CZ/maUDbJPXZUr7AR+Oa7NXgd/621KjP28dUJ0u21/oDRqFB3Cz/x/lWaAkbv7n/flPhl1j2A+OBNvk41z+Yn/5A8CMuPlnAe3+/wgVYf+uBLfRPwLfBN4KjAHajvGH+YTbCBgJHPLXe3vc/NHARn/+BWG3RZLarzfYrj+B74hk+wEfAH4BzO4zvwZ40f9dv0mX7S/0Bk33B5AHHPT/g5zaz/ur/PcWhl1ryO10osH2kL/8Z/t576f+e18I+3cF3GbH+sN8wm0EfMmff18/61zjv/fHsH97ktpvKMGWMe0X97vO8n9XG5AXNz9ltz+dYxu+s4FyvFuav9TP+/f4z29NXknpzcwKgYv8yXv6WSTj23QYbXTFIOsswfvjdbGZFQy7yGjKxPZb5T/nA5WQ+ttfznBWFgBO8Z9fHOD93vknJ6GWdPAhM6vEO76+Ae9fbq/3WWYm3v9E+51ztf18htp06G004PbqnOswszXAacBJwOoE1ZrqLjKzBXjnymuBPznnVg6wbCa231T/uROo91+n9PanPbbhm+g/9/cfN37+pCTUkg7+H/Bx4JN4hys2mdnX+iwzaJs655rxDv+OMLPSgOpMdSfcRmZWhnd0YcD1yMzt9X3AZ4CPAd8CVpjZPX17AmZw+33Gf37YOdfuv07p7U/BNny9G3/LAO83+8+Z+ge419N4f0CmAUV4/+L7Kl7vqG+a2Wfilj1Wm4LadShtFP+HWtsrbAK+CMzFa5sJwN8CO/F67v13n+Uzrv3M7HLgQ3h7a/H/AE3p7U+HIiUpnHNf7zNrA/AdM1sBPALcZGb/6ZxrTX51komcc3f1mdUM/MbMngBeAa42szOdc88lv7rwmdks4C7AgBudc6uOsUrK0B7b8B32n4sGeL/Yf25KQi1pxzn3KLACqADO8Gcfq01B7TqUNjoc95621wE453YDd/iT8QMHZEz7+QNLPAyMAH7inPtpn0VSevtTsA1fb8eH8QO83zt/exJqSVcb/eca/3nQNjWzYrwgbHDOpfUfkGE44TZyzjXiXUM04Hpoe+3Vd5vMmPYzs5HAo3jnue7AO1zbV0pvfwq24evdPX/TAO/3zo9KD6kgjPCfe4+vr8e7wHPUAEOSqU2H3kYDbq9mlgvMw+tyvSFBdaarvttkr0i3n99h5k/AHOBe4CPOv8isj5Te/hRsw7cM718h0/wuw329w3/+Y9IqSiNmNgo41598EcA/z/a4P++6flbL+DYdRhst6fN+vCvxurz/xTnXNuwi05SZGd7FwvDGbumRbT8zywfuBxbhnfd+j3Ouu79lU377C/uq9ig8ODKk1jKgOG6+htTy2mExcDWQ3Wf+ZLxx+hxwf5/3Bhuup40IDqnVT7sNZ0itftuIgYc0qiaNh4Q60fYDRuFdclLaZ34J8HO/HXYDRZnQfkA23h6aw+vBXHQc66Ts9hd6g0bhgfevjOc4ehDk3umMHwQZuD7uD8US4H/8QGv1569h8AFWm/EGWH2IaA+CfIW/3fQ+evzfHz/viuG2EV5X9m7/8x8HfseRQWh/HHY7JKP98P5R5fA6KTzub5OP+n+knd8eZ2dK++Fdq+b8x730Pyj0nUBVOmx/oTdoVB5AId4ArJvwjj339qwaH3ZtYT+A2cC/Ayvxgr4T7+LN5Xh7tYWDrHs9Xq/JZn/j/xOwOOzfFFA7XR/3x2Wgx/WJaCO8oeD+5C/fDLwAfCDsNkhW++FdJ/U9vDFMa/H2MJrx/pH1I2BcJrUfR8bNPNZjcjpsf+Z/iYiISCSo84iIiESKgk1ERCJFwSYiIpGiYBMRkUhRsImISKQo2EREJFIUbCIiEikKNhERiRQFm4iIRIqCTUREIkXBJiIikaJgEwmQmbnjeNwZdp3HYmY3+bVeH3YtIseSE3YBIhniV4O8tzRpVYhkAAWbSBI4564PuwaRTKFDkSIiEikKNpEU45/L2mZmeWb2z2a22czazGyLmX3TzAoGWK/SzH5oZhv95evN7GEzu2SQ76o0s2+b2Stm1mxmjf7rH5hZzQDrzDezB8yswV/nKTNbnKjfLzJcCjaR1GTA74EbgVeBJcBI4GvAg2aWfdTCZuOA54EvAnnAfcBLwMXAI2b2uTd8gdls4GXgK0AV8AjwF/+7bwTO6Keu04DngMn+8huB84DHzGze0H+uSOLoHJtIapqI9w/Pec65LQBmNgp4HHgz8Gnglrjlfw5MBX4D3OCc6/DXOQcvgH5oZk8451725+cAfwDG+5/zj73r+O/PBdr6qeuTwGecc/8at+y/AJ8FvgS8f3g/W2T4tMcmkgTH6O5/9QCrfbM31ACcc/vx9qQAPhX32VOBK4HDwKfjA8o5txQv9LLxQqnX24GZwFrgi/Hr+Outdc5t7qemZfGh5rvZfz5vgN8hklTaYxNJjsG6+78+wPz/6zvDOfewmTUA08ysxjm3GzjHf/th51x9P5/z38DngXPj5l3sP9/mnOsevPSjPNpPTXVmVg/0e05OJNkUbCJJMITu/g3OuaYB3tsOjADGArv9Z4BtAyzfO39c3LwJ/nN/e2WDqR1gfhPeOUCR0OlQpEj0uQR+Vk8CP0skEAo2kdQ0wsxKB3hvov+8q8/zpAGWn+w/74ybt8N/njak6kRSmIJNJHW9s+8M/5q0kcAW//waHBmS6zIzq+jnc/7Of34mbt5f/OcPmZn+DkikaIMWSV3fMLPJvRNmVgX80J+8tXe+33NyCVAK/NTMcuPWOQv4ONAdvw5wL7ABmAf8IH4df725fm9LkbSjziMiSXCMEfxfd859ve88YDWw1sweAzqBi4AK4Amgb5f7j+Htkb0fON/MlgOjgAvwuvp/ofcaNgDnXJeZXQv8GfgC8F5/HQNm4AXeNcAWRNKMgk0kOT4wyHurgL7B5oB3+PPfy5EekLcC33bOdR21sHM7zex04J+Aq/GuU2sBHgN+7Jzrr5v+GjM7Be/auKuAy4F2vFD9Pt4IIyJpx5xLZIcpERkuM3PAdufc5LBrEUlHOscmIiKRomATEZFIUbCJiEik6BybiIhEivbYREQkUhRsIiISKQo2ERGJFAWbiIhEioJNREQiRcEmIiKRomATEZFIUbCJiEikKNhERCRSFGwiIhIpCjYREYkUBZuIiESKgk1ERCJFwSYiIpHy/wFyGQySJDmR+wAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 450x300 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 200: loss=0.23569\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"loss_hist = train(x_train, y_train, lr=2e-4, nb_epochs=nb_epochs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training accuracy: 0.982\\n\",\n      \"Test accuracy: 0.692\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Training accuracy: %.3f\\\"%(compute_classification_accuracy(x_train,y_train)))\\n\",\n    \"print(\\\"Test accuracy: %.3f\\\"%(compute_classification_accuracy(x_test,y_test)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def get_mini_batch(x_data, y_data, shuffle=False):\\n\",\n    \"    for ret in sparse_data_generator_from_hdf5_spikes(x_data, y_data, batch_size, nb_steps, nb_inputs, max_time, shuffle=shuffle):\\n\",\n    \"        return ret \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x_batch, y_batch = get_mini_batch(x_test, y_test)\\n\",\n    \"output, other_recordings = run_snn(x_batch.to_dense())\\n\",\n    \"mem_rec, spk_rec = other_recordings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 600x400 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig=plt.figure(dpi=100)\\n\",\n    \"plot_voltage_traces(output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1050x450 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Let's plot the hiddden layer spiking activity for some input stimuli\\n\",\n    \"\\n\",\n    \"nb_plt = 4\\n\",\n    \"gs = GridSpec(1,nb_plt)\\n\",\n    \"fig= plt.figure(figsize=(7,3),dpi=150)\\n\",\n    \"for i in range(nb_plt):\\n\",\n    \"    plt.subplot(gs[i])\\n\",\n    \"    plt.imshow(spk_rec[i].detach().cpu().numpy().T,cmap=plt.cm.gray_r, origin=\\\"lower\\\" )\\n\",\n    \"    if i==0:\\n\",\n    \"        plt.xlabel(\\\"Time\\\")\\n\",\n    \"        plt.ylabel(\\\"Units\\\")\\n\",\n    \"\\n\",\n    \"    sns.despine()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We see that spiking in the hidden layer is quite sparse as in the previous Tutorial 3 because we used the same activity regularizer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a rel=\\\"license\\\" href=\\\"http://creativecommons.org/licenses/by/4.0/\\\"><img alt=\\\"Creative Commons License\\\" style=\\\"border-width:0\\\" src=\\\"https://i.creativecommons.org/l/by/4.0/88x31.png\\\" /></a><br />This work is licensed under a <a rel=\\\"license\\\" href=\\\"http://creativecommons.org/licenses/by/4.0/\\\">Creative Commons Attribution 4.0 International License</a>.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.9\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebooks/SpyTorchTutorial5.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tutorial 5: Training a spiking neural network on simulated touch sensor data of Braille letters\\n\",\n    \"\\n\",\n    \"Friedemann Zenke (https://fzenke.net)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In this tutorial we will apply what we have learned previously to a neuromorphic tactile dataset of Braille letters.\\n\",\n    \"\\n\",\n    \"For details on the dataset and touch sensor, please see:\\n\",\n    \"\\n\",\n    \"> Muller-Cleve, S.F., Fra, V., Khacef, L., Pequeno-Zurro, A., Klepatsch, D., Forno, E., Ivanovich, D.G., Rastogi, S., Urgese, G., Zenke, F., and Bartolozzi, C. (2022). Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware (arXiv).\\n\",\n    \"> https://arxiv.org/abs/2205.15864\\n\",\n    \"\\n\",\n    \"The dataset is publicly available as: \\n\",\n    \"\\n\",\n    \"> Müller-Cleve Simon F. (2022). Tactile Braille Letters Dataset (Version 1) [Data set]. Zenodo. \\n\",\n    \"> https://doi.org/10.5281/zenodo.6556273\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os, pickle, gzip\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib.gridspec import GridSpec\\n\",\n    \"import seaborn as sns\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"from torch.utils.data import TensorDataset, DataLoader\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<torch._C.Generator at 0x7f2c0d841270>\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dtype = torch.float\\n\",\n    \"\\n\",\n    \"# Check whether a GPU is available\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    device = torch.device(\\\"cuda\\\")     \\n\",\n    \"else:\\n\",\n    \"    device = torch.device(\\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"# Seed random number generators to ensure reproducibility\\n\",\n    \"seed=42\\n\",\n    \"np.random.seed(seed)\\n\",\n    \"torch.manual_seed(seed)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/tmp/ipykernel_1864084/4087015317.py:23: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  ../torch/csrc/utils/tensor_new.cpp:210.)\\n\",\n      \"  data = torch.tensor( [ d[:l] for d in data ], dtype=dtype )\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# data structure: [trial number] x ['key'] x [time] x [sensor_nr]\\n\",\n    \"\\n\",\n    \"file_name = 'data/tutorial5_braille_spiking_data.pkl.gz'\\n\",\n    \"with gzip.open(file_name, 'rb') as infile:\\n\",\n    \"    data_dict = pickle.load(infile)\\n\",\n    \"\\n\",\n    \"letter_written = ['Space', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',\\n\",\n    \"                  'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\\n\",\n    \"\\n\",\n    \"# Extract data\\n\",\n    \"nb_repetitions=50\\n\",\n    \"data = []\\n\",\n    \"labels = []\\n\",\n    \"for i, letter in enumerate(letter_written):\\n\",\n    \"    for repetition in np.arange(nb_repetitions):\\n\",\n    \"        idx = i*nb_repetitions+repetition\\n\",\n    \"        dat = 1.0-data_dict[idx]['taxel_data'][:]/255\\n\",\n    \"        data.append(dat)\\n\",\n    \"        labels.append(i)\\n\",\n    \"        \\n\",\n    \"# Crop to same length\\n\",\n    \"data_steps = l = np.min([ len(d) for d in data ])\\n\",\n    \"data = torch.tensor( [ d[:l] for d in data ], dtype=dtype )    \\n\",\n    \"labels = torch.tensor(labels,dtype=torch.long)\\n\",\n    \"\\n\",\n    \"# Select nonzero inputs\\n\",\n    \"nzid = [1,2,6,10]\\n\",\n    \"data = data[:,:,nzid]\\n\",\n    \"\\n\",\n    \"# Standardize data\\n\",\n    \"rshp = data.reshape((-1,data.shape[2]))\\n\",\n    \"data = (data-rshp.mean(0))/(rshp.std(0)+1e-3)\\n\",\n    \"\\n\",\n    \"# Upsample\\n\",\n    \"def upsample(data, n=2):\\n\",\n    \"    shp=data.shape\\n\",\n    \"    tmp=data.reshape(shp+(1,))\\n\",\n    \"    tmp=data.tile((1,1,1,n))\\n\",\n    \"    return tmp.reshape((shp[0],n*shp[1],shp[2]))\\n\",\n    \"\\n\",\n    \"nb_upsample = 2\\n\",\n    \"data = upsample(data,n=nb_upsample)\\n\",\n    \"    \\n\",\n    \"# Shuffle data\\n\",\n    \"idx = np.arange(len(data))\\n\",\n    \"np.random.shuffle(idx)\\n\",\n    \"data = data[idx]\\n\",\n    \"labels = labels[idx]\\n\",\n    \"\\n\",\n    \"# Peform train/test split\\n\",\n    \"a = int(0.8*len(idx))\\n\",\n    \"x_train, x_test = data[:a], data[a:]\\n\",\n    \"y_train, y_test = labels[:a], labels[a:]\\n\",\n    \"\\n\",\n    \"ds_train = TensorDataset(x_train,y_train)\\n\",\n    \"ds_test = TensorDataset(x_test,y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\\n\",\n       \"       17, 18, 19, 20, 21, 22, 23, 24, 25, 26])\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.unique(labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Visualize single data point\\n\",\n    \"i = 123\\n\",\n    \"plt.plot(data[i])\\n\",\n    \"plt.title(\\\"Letter %s\\\"%letter_written[labels[i]])\\n\",\n    \"sns.despine()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setup of the spiking network model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Number of training data 1080\\n\",\n      \"Number of testing data 270\\n\",\n      \"Number of outputs 28\\n\",\n      \"Number of timesteps 152\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"nb_channels = len(nzid)\\n\",\n    \"enc_fan_out = 32  # Num of spiking neurons used to encode each channel\\n\",\n    \"\\n\",\n    \"# Network parameters\\n\",\n    \"nb_inputs  = nb_channels*enc_fan_out\\n\",\n    \"nb_hidden  = 200\\n\",\n    \"nb_outputs = len(np.unique(labels))+1\\n\",\n    \"time_step = 2e-3/nb_upsample # TODO needs to be updated to reflect the correct time scale\\n\",\n    \"nb_steps = nb_upsample*data_steps  # TODO We should change this and upsample the input data\\n\",\n    \"\\n\",\n    \"batch_size = 100\\n\",\n    \"\\n\",\n    \"print(\\\"Number of training data %i\\\"%len(ds_train))\\n\",\n    \"print(\\\"Number of testing data %i\\\"%len(ds_test))\\n\",\n    \"print(\\\"Number of outputs %i\\\"%nb_outputs)\\n\",\n    \"print(\\\"Number of timesteps %i\\\"%nb_steps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tau_mem = 20e-3\\n\",\n    \"tau_syn = 10e-3\\n\",\n    \"\\n\",\n    \"alpha   = float(np.exp(-time_step/tau_syn))\\n\",\n    \"beta    = float(np.exp(-time_step/tau_mem))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"init done\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"encoder_weight_scale = 1.0\\n\",\n    \"fwd_weight_scale = 3.0\\n\",\n    \"rec_weight_scale = 1e-2*fwd_weight_scale\\n\",\n    \"\\n\",\n    \"# Parameters\\n\",\n    \"\\n\",\n    \"# Encoder \\n\",\n    \"enc_gain = torch.empty((nb_inputs,),  device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"enc_bias = torch.empty((nb_inputs,),  device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"torch.nn.init.normal_(enc_gain, mean=0.0, std=encoder_weight_scale) # TODO update this parameter\\n\",\n    \"torch.nn.init.normal_(enc_bias, mean=0.0, std=1.0)\\n\",\n    \"\\n\",\n    \"# Spiking network\\n\",\n    \"w1 = torch.empty((nb_inputs, nb_hidden),  device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"torch.nn.init.normal_(w1, mean=0.0, std=fwd_weight_scale/np.sqrt(nb_inputs))\\n\",\n    \"\\n\",\n    \"w2 = torch.empty((nb_hidden, nb_outputs), device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"torch.nn.init.normal_(w2, mean=0.0, std=fwd_weight_scale/np.sqrt(nb_hidden))\\n\",\n    \"\\n\",\n    \"v1 = torch.empty((nb_hidden, nb_hidden), device=device, dtype=dtype, requires_grad=True)\\n\",\n    \"torch.nn.init.normal_(v1, mean=0.0, std=rec_weight_scale/np.sqrt(nb_hidden))\\n\",\n    \"\\n\",\n    \"print(\\\"init done\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_voltage_traces(mem, spk=None, dim=(3,5), spike_height=5, **kwargs):\\n\",\n    \"    gs=GridSpec(*dim)\\n\",\n    \"    if spk is not None:\\n\",\n    \"        dat = 1.0*mem\\n\",\n    \"        dat[spk>0.0] = spike_height\\n\",\n    \"        dat = dat.detach().cpu().numpy()\\n\",\n    \"    else:\\n\",\n    \"        dat = mem.detach().cpu().numpy()\\n\",\n    \"    for i in range(np.prod(dim)):\\n\",\n    \"        if i==0: a0=ax=plt.subplot(gs[i])\\n\",\n    \"        else: ax=plt.subplot(gs[i],sharey=a0)\\n\",\n    \"        ax.plot(dat[i], **kwargs)\\n\",\n    \"        ax.axis(\\\"off\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from IPython.display import clear_output\\n\",\n    \"\\n\",\n    \"def live_plot(loss):\\n\",\n    \"    if len(loss) == 1:\\n\",\n    \"        return\\n\",\n    \"    clear_output(wait=True)\\n\",\n    \"    ax = plt.figure(figsize=(3,2), dpi=150).gca()\\n\",\n    \"    ax.plot(range(1, len(loss) + 1), loss)\\n\",\n    \"    ax.set_xlabel(\\\"Epoch\\\")\\n\",\n    \"    ax.set_ylabel(\\\"Loss\\\")\\n\",\n    \"    ax.xaxis.get_major_locator().set_params(integer=True)\\n\",\n    \"    sns.despine()\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the network\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class SurrGradSpike(torch.autograd.Function):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Here we implement our spiking nonlinearity which also implements \\n\",\n    \"    the surrogate gradient. By subclassing torch.autograd.Function, \\n\",\n    \"    we will be able to use all of PyTorch's autograd functionality.\\n\",\n    \"    Here we use the normalized negative part of a fast sigmoid \\n\",\n    \"    as this was done in Zenke & Ganguli (2018).\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    scale = 20.0 # controls steepness of surrogate gradient\\n\",\n    \"\\n\",\n    \"    @staticmethod\\n\",\n    \"    def forward(ctx, input):\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        In the forward pass we compute a step function of the input Tensor\\n\",\n    \"        and return it. ctx is a context object that we use to stash information which \\n\",\n    \"        we need to later backpropagate our error signals. To achieve this we use the \\n\",\n    \"        ctx.save_for_backward method.\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        ctx.save_for_backward(input)\\n\",\n    \"        out = torch.zeros_like(input)\\n\",\n    \"        out[input > 0] = 1.0\\n\",\n    \"        return out\\n\",\n    \"\\n\",\n    \"    @staticmethod\\n\",\n    \"    def backward(ctx, grad_output):\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        In the backward pass we receive a Tensor we need to compute the \\n\",\n    \"        surrogate gradient of the loss with respect to the input. \\n\",\n    \"        Here we use the normalized negative part of a fast sigmoid \\n\",\n    \"        as this was done in Zenke & Ganguli (2018).\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        input, = ctx.saved_tensors\\n\",\n    \"        grad_input = grad_output.clone()\\n\",\n    \"        grad = grad_input/(SurrGradSpike.scale*torch.abs(input)+1.0)**2\\n\",\n    \"        return grad\\n\",\n    \"    \\n\",\n    \"# here we overwrite our naive spike function by the \\\"SurrGradSpike\\\" nonlinearity which implements a surrogate gradient\\n\",\n    \"spike_fn  = SurrGradSpike.apply\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def run_snn(inputs):\\n\",\n    \"    bs = inputs.shape[0]\\n\",\n    \"    enc = torch.zeros((bs,nb_inputs), device=device, dtype=dtype)\\n\",\n    \"    input_spk = torch.zeros((bs,nb_inputs), device=device, dtype=dtype)\\n\",\n    \"    syn = torch.zeros((bs,nb_hidden), device=device, dtype=dtype)\\n\",\n    \"    mem = -1e-3*torch.ones((bs,nb_hidden), device=device, dtype=dtype)\\n\",\n    \"    out = torch.zeros((bs,nb_hidden), device=device, dtype=dtype)\\n\",\n    \"    \\n\",\n    \"    enc_rec = []\\n\",\n    \"    mem_rec = []\\n\",\n    \"    spk_rec = [] \\n\",\n    \"    \\n\",\n    \"    # encoder_currents = torch.einsum(\\\"abc,c->ab\\\", (inputs.tile((enc_fan_out,)), enc_gain))+enc_bias\\n\",\n    \"    encoder_currents = enc_gain*(inputs.tile((enc_fan_out,))+enc_bias)\\n\",\n    \"    for t in range(nb_steps):\\n\",\n    \"        # Compute encoder activity\\n\",\n    \"        new_enc = (beta*enc +(1.0-beta)*encoder_currents[:,t])*(1.0-input_spk.detach())\\n\",\n    \"        input_spk = spike_fn(enc-1.0)\\n\",\n    \"        \\n\",\n    \"        # Compute hidden layer activity\\n\",\n    \"        h1 = input_spk.mm(w1) + torch.einsum(\\\"ab,bc->ac\\\", (out, v1))\\n\",\n    \"        mthr = mem-1.0\\n\",\n    \"        out = spike_fn(mthr)\\n\",\n    \"        rst = out.detach() # We do not want to backprop through the reset\\n\",\n    \"\\n\",\n    \"        new_syn = alpha*syn +h1\\n\",\n    \"        new_mem =(beta*mem +(1.0-beta)*syn)*(1.0-rst)\\n\",\n    \"\\n\",\n    \"        # Here we store some state variables so we can look at them later.\\n\",\n    \"        mem_rec.append(mem)\\n\",\n    \"        spk_rec.append(out)\\n\",\n    \"        enc_rec.append(enc)\\n\",\n    \"        \\n\",\n    \"        enc = new_enc\\n\",\n    \"        mem = new_mem\\n\",\n    \"        syn = new_syn\\n\",\n    \"\\n\",\n    \"    enc_rec = torch.stack(enc_rec,dim=1)  \\n\",\n    \"    mem_rec = torch.stack(mem_rec,dim=1)\\n\",\n    \"    spk_rec = torch.stack(spk_rec,dim=1)\\n\",\n    \"\\n\",\n    \"    # Readout layer\\n\",\n    \"    h2= torch.einsum(\\\"abc,cd->abd\\\", (spk_rec, w2))\\n\",\n    \"    flt = torch.zeros((bs,nb_outputs), device=device, dtype=dtype)\\n\",\n    \"    out = torch.zeros((bs,nb_outputs), device=device, dtype=dtype)\\n\",\n    \"    out_rec = [out]\\n\",\n    \"    for t in range(nb_steps):\\n\",\n    \"        new_flt = alpha*flt +h2[:,t]\\n\",\n    \"        new_out = beta*out +(1.0-beta)*flt\\n\",\n    \"\\n\",\n    \"        flt = new_flt\\n\",\n    \"        out = new_out\\n\",\n    \"\\n\",\n    \"        out_rec.append(out)\\n\",\n    \"\\n\",\n    \"    out_rec = torch.stack(out_rec,dim=1)\\n\",\n    \"    other_recs = [enc_rec.detach(), mem_rec.detach(), spk_rec.detach()]\\n\",\n    \"    return out_rec, other_recs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def train(dataset, lr=1e-3, nb_epochs=10):\\n\",\n    \"    \\n\",\n    \"    params = [enc_gain, enc_bias, w1,w2,v1]\\n\",\n    \"    optimizer = torch.optim.Adamax(params, lr=lr, betas=(0.9,0.995))\\n\",\n    \"\\n\",\n    \"    log_softmax_fn = nn.LogSoftmax(dim=1)\\n\",\n    \"    loss_fn = nn.NLLLoss()\\n\",\n    \"    \\n\",\n    \"    generator = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=2)\\n\",\n    \"    \\n\",\n    \"    loss_hist = []\\n\",\n    \"    for e in range(nb_epochs):\\n\",\n    \"        local_loss = []\\n\",\n    \"        for x_local, y_local in generator:\\n\",\n    \"            x_local, y_local = x_local.to(device), y_local.to(device)\\n\",\n    \"            output,recs = run_snn(x_local)\\n\",\n    \"            _,_,spks=recs\\n\",\n    \"            m,_=torch.max(output,1)\\n\",\n    \"            log_p_y = log_softmax_fn(m)\\n\",\n    \"            \\n\",\n    \"            # Here we can set up our regularizer loss\\n\",\n    \"            reg_loss = 1e-3*torch.mean(torch.sum(spks,1)) # e.g., L1 loss on total number of spikes\\n\",\n    \"            # reg_loss = 0.0\\n\",\n    \"            \\n\",\n    \"            # Here we combine supervised loss and the regularizer\\n\",\n    \"            loss_val = loss_fn(log_p_y, y_local) + reg_loss\\n\",\n    \"\\n\",\n    \"            optimizer.zero_grad()\\n\",\n    \"            loss_val.backward()\\n\",\n    \"            optimizer.step()\\n\",\n    \"            local_loss.append(loss_val.item())\\n\",\n    \"        \\n\",\n    \"        mean_loss = np.mean(local_loss)\\n\",\n    \"        loss_hist.append(mean_loss)\\n\",\n    \"        live_plot(loss_hist)\\n\",\n    \"        print(\\\"Epoch %i: loss=%.5f\\\"%(e+1,mean_loss))\\n\",\n    \"        \\n\",\n    \"    return loss_hist\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"def compute_classification_accuracy(dataset):\\n\",\n    \"    \\\"\\\"\\\" Computes classification accuracy on supplied data in batches. \\\"\\\"\\\"\\n\",\n    \"    generator = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=2)\\n\",\n    \"    accs = []\\n\",\n    \"    for x_local, y_local in generator:\\n\",\n    \"        x_local, y_local = x_local.to(device), y_local.to(device)\\n\",\n    \"        output,_ = run_snn(x_local)\\n\",\n    \"        m,_= torch.max(output,1) # max over time\\n\",\n    \"        _,am=torch.max(m,1)      # argmax over output units\\n\",\n    \"        tmp = np.mean((y_local==am).detach().cpu().numpy()) # compare to labels\\n\",\n    \"        accs.append(tmp)\\n\",\n    \"    return np.mean(accs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"nb_epochs = 100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 450x300 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 100: loss=0.05176\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"loss_hist = train(ds_train, lr=1e-2, nb_epochs=nb_epochs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training accuracy: 0.994\\n\",\n      \"Test accuracy: 0.967\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Training accuracy: %.3f\\\"%(compute_classification_accuracy(ds_train)))\\n\",\n    \"print(\\\"Test accuracy: %.3f\\\"%(compute_classification_accuracy(ds_test)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Let's run the network on a single batch from the test set\\n\",\n    \"data_loader = DataLoader(ds_test, batch_size=batch_size, shuffle=False)\\n\",\n    \"x_batch, y_batch = next(iter(data_loader))\\n\",\n    \"output, other_recordings = run_snn(x_batch.to(device))\\n\",\n    \"enc_rec, mem_rec, spk_rec = other_recordings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1050x450 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# This is how our spiking encoders convert the current based input into spike trains (we plot 20)\\n\",\n    \"\\n\",\n    \"fig=plt.figure(dpi=150,figsize=(7,3))\\n\",\n    \"plot_voltage_traces(enc_rec[:,:,:20], color=\\\"black\\\", alpha=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1050x450 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Let's take a look at the readout layer activity \\n\",\n    \"\\n\",\n    \"fig=plt.figure(dpi=150,figsize=(7,3))\\n\",\n    \"plot_voltage_traces(output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1050x450 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Let's plot the hiddden layer spiking activity for some input stimuli\\n\",\n    \"\\n\",\n    \"nb_plt = 4\\n\",\n    \"gs = GridSpec(1,nb_plt)\\n\",\n    \"fig= plt.figure(figsize=(7,3),dpi=150)\\n\",\n    \"for i in range(nb_plt):\\n\",\n    \"    plt.subplot(gs[i])\\n\",\n    \"    plt.imshow(spk_rec[i].detach().cpu().numpy().T,cmap=plt.cm.gray_r, origin=\\\"lower\\\" )\\n\",\n    \"    if i==0:\\n\",\n    \"        plt.xlabel(\\\"Time\\\")\\n\",\n    \"        plt.ylabel(\\\"Units\\\")\\n\",\n    \"\\n\",\n    \"    sns.despine()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a rel=\\\"license\\\" href=\\\"http://creativecommons.org/licenses/by/4.0/\\\"><img alt=\\\"Creative Commons License\\\" style=\\\"border-width:0\\\" src=\\\"https://i.creativecommons.org/l/by/4.0/88x31.png\\\" /></a><br />This work is licensed under a <a rel=\\\"license\\\" href=\\\"http://creativecommons.org/licenses/by/4.0/\\\">Creative Commons Attribution 4.0 International License</a>.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebooks/figures/.gitignore",
    "content": "## Core latex/pdflatex auxiliary files:\n*.aux\n*.lof\n*.log\n*.lot\n*.fls\n*.out\n*.toc\n*.fmt\n*.fot\n*.cb\n*.cb2\n.*.lb\n\n## Intermediate documents:\n*.dvi\n*.xdv\n*-converted-to.*\n# these rules might exclude image files for figures etc.\n# *.ps\n# *.eps\n# *.pdf\n\n## Generated if empty string is given at \"Please type another file name for output:\"\n.pdf\n\n## Bibliography auxiliary files (bibtex/biblatex/biber):\n*.bbl\n*.bcf\n*.blg\n*-blx.aux\n*-blx.bib\n*.run.xml\n\n## Build tool auxiliary files:\n*.fdb_latexmk\n*.synctex\n*.synctex(busy)\n*.synctex.gz\n*.synctex.gz(busy)\n*.pdfsync\n\n## Build tool directories for auxiliary files\n# latexrun\nlatex.out/\n\n## Auxiliary and intermediate files from other packages:\n# algorithms\n*.alg\n*.loa\n\n# achemso\nacs-*.bib\n\n# amsthm\n*.thm\n\n# beamer\n*.nav\n*.pre\n*.snm\n*.vrb\n\n# changes\n*.soc\n\n# comment\n*.cut\n\n# cprotect\n*.cpt\n\n# elsarticle (documentclass of Elsevier journals)\n*.spl\n\n# endnotes\n*.ent\n\n# fixme\n*.lox\n\n# feynmf/feynmp\n*.mf\n*.mp\n*.t[1-9]\n*.t[1-9][0-9]\n*.tfm\n\n#(r)(e)ledmac/(r)(e)ledpar\n*.end\n*.?end\n*.[1-9]\n*.[1-9][0-9]\n*.[1-9][0-9][0-9]\n*.[1-9]R\n*.[1-9][0-9]R\n*.[1-9][0-9][0-9]R\n*.eledsec[1-9]\n*.eledsec[1-9]R\n*.eledsec[1-9][0-9]\n*.eledsec[1-9][0-9]R\n*.eledsec[1-9][0-9][0-9]\n*.eledsec[1-9][0-9][0-9]R\n\n# glossaries\n*.acn\n*.acr\n*.glg\n*.glo\n*.gls\n*.glsdefs\n\n# gnuplottex\n*-gnuplottex-*\n\n# gregoriotex\n*.gaux\n*.gtex\n\n# htlatex\n*.4ct\n*.4tc\n*.idv\n*.lg\n*.trc\n*.xref\n\n# hyperref\n*.brf\n\n# knitr\n*-concordance.tex\n# TODO Comment the next line if you want to keep your tikz graphics files\n*.tikz\n*-tikzDictionary\n\n# listings\n*.lol\n\n# makeidx\n*.idx\n*.ilg\n*.ind\n*.ist\n\n# minitoc\n*.maf\n*.mlf\n*.mlt\n*.mtc[0-9]*\n*.slf[0-9]*\n*.slt[0-9]*\n*.stc[0-9]*\n\n# minted\n_minted*\n*.pyg\n\n# morewrites\n*.mw\n\n# nomencl\n*.nlg\n*.nlo\n*.nls\n\n# pax\n*.pax\n\n# pdfpcnotes\n*.pdfpc\n\n# sagetex\n*.sagetex.sage\n*.sagetex.py\n*.sagetex.scmd\n\n# scrwfile\n*.wrt\n\n# sympy\n*.sout\n*.sympy\nsympy-plots-for-*.tex/\n\n# pdfcomment\n*.upa\n*.upb\n\n# pythontex\n*.pytxcode\npythontex-files-*/\n\n# tcolorbox\n*.listing\n\n# thmtools\n*.loe\n\n# TikZ & PGF\n*.dpth\n*.md5\n*.auxlock\n\n# todonotes\n*.tdo\n\n# vhistory\n*.hst\n*.ver\n\n# easy-todo\n*.lod\n\n# xcolor\n*.xcp\n\n# xmpincl\n*.xmpi\n\n# xindy\n*.xdy\n\n# xypic precompiled matrices\n*.xyc\n\n# endfloat\n*.ttt\n*.fff\n\n# Latexian\nTSWLatexianTemp*\n\n## Editors:\n# WinEdt\n*.bak\n*.sav\n\n# Texpad\n.texpadtmp\n\n# LyX\n*.lyx~\n\n# Kile\n*.backup\n\n# KBibTeX\n*~[0-9]*\n\n# auto folder when using emacs and auctex\n./auto/*\n*.el\n\n# expex forward references with \\gathertags\n*-tags.tex\n\n# standalone packages\n*.sta\n\n"
  },
  {
    "path": "notebooks/figures/mlp_sketch/Makefile",
    "content": "all: mlp_sketch.png\n\n%.png: %.pdf\n\tconvert -density 150 $< $@\n\n%.pdf: %.tex\n\tpdflatex $< \n\n"
  },
  {
    "path": "notebooks/figures/mlp_sketch/mlp_sketch.tex",
    "content": "\\documentclass{standalone}\n\n\n\\usepackage{tikz}\n\\usepackage{verbatim}\n\\usepackage{tikz,graphicx}\n\\usepackage{sfmath}\n\\usepackage{xifthen}\n\\usetikzlibrary{positioning,arrows,calc}\n% \\usetikzlibrary{spy}\n\\renewcommand\\familydefault\\sfdefault\n\n\\usepackage{xcolor}\n\n\\definecolor{gnu-violet}{HTML}{9400D3}%\n\\definecolor{gnu-green}{HTML}{009E73}%\n\\definecolor{gnu-blue}{HTML}{56b4e9}%\n\\definecolor{gnu-yellow}{HTML}{E69F00}%\n\n\n\\begin{document}\n\n\\pagestyle{empty}\n\n\\def\\layersep{1.2cm}\n\\def\\unitsep{0.6cm}\n\\def\\nbhidden{1}\n\n\\begin{tikzpicture}[shorten >=1pt,->,draw=black!50]\n\n    \\tikzstyle{every pin edge}=[<-,shorten <=1pt]\n    \\tikzstyle{input neuron}=[circle, fill=black!25, minimum size=15pt, inner sep=0pt]\n    \\tikzstyle{neuron}=[circle,fill=black!25,minimum size=12pt,inner sep=0pt]\n    \\tikzstyle{annot} = [text width=2cm, text centered, node distance=2.2cm]\n\n\n\t% Input units\n\t\\foreach \\name / \\y in {0,...,5} {\n\t\t\\node[neuron] (I-\\name) at (\\y*\\unitsep, 0) {};\n\t}\n\n    % Draw the hidden layer nodes \n\t\\foreach \\layer in {1,...,\\nbhidden} {\n\t\t\\foreach \\name / \\y in {1,...,4} {\n\t\t\t\\node[neuron] (H\\layer-\\name) at (\\y*\\unitsep, \\layer*\\layersep) {};\n\t\t}\n\t}\n\n    % Draw the output layer nodes\n\t\\foreach \\name / \\y in {2,...,3} {\n\t\t\\node[neuron] (O-\\name) at (\\y*\\unitsep, \\nbhidden*\\layersep + \\layersep) {};\n\t}\n\n    % Connect every node in the input layer with every node in the\n    % hidden layer.\n    \\foreach \\source in {0,...,5}\n\t\t\\foreach \\dest in {1,...,4} {\n\t\t\t\\path (I-\\source) edge [->] (H1-\\dest);\n\t\t}\n\n    % Connect input to hidden\n    \\foreach \\source in {1,...,4}\n\t\t\\foreach \\dest in {2,...,3} {\n\t\t\t\\path (H\\nbhidden-\\source) edge [->] (O-\\dest);\n\t\t}\n\n    % Connect hidden layers\n\t% \\foreach \\layer in {1,...,\\nbhidden} {\n    % \\foreach \\source in {1,...,4}\n\t% \t\\foreach \\dest in {1,...,4} {\n\t% \t\t\\path (H\\layer-\\source) edge [->] (H\\nbhidden-\\dest);\n\t% \t}\n\t% }\n\n    % Connect every node in the hidden layer with the output layer\n    \\foreach \\source in {1,...,4}\n\t\t\\foreach \\dest in {2,...,3} {\n\t\t\t\\path (H\\nbhidden-\\source) edge [->] (O-\\dest);\n\t\t}\n\n\n    % Annotate the layers\n\t\\node[annot,left of=I-0] {Input layer};\n\t\\node[annot,left of=O-2] {Output layer};\n\n\n\\end{tikzpicture}\n\n% End of code\n\n\\end{document}\n\n\n\n"
  },
  {
    "path": "notebooks/figures/snn_graph/Makefile",
    "content": "all: snn_graph.png\n\n%.pdf: %.tex\n\tpdflatex $<\n\n%.png: %.pdf\n\tconvert -density 150 $< $@\n"
  },
  {
    "path": "notebooks/figures/snn_graph/snn_graph.tex",
    "content": "\\documentclass{standalone}\n\n\\usepackage[latin1]{inputenc}\n\\usepackage{tikz}\n\\usepackage{tikz,graphicx}\n\\usepackage{sfmath}\n\\usepackage{bbold}\n\\usepackage{dsfont}\n\\usetikzlibrary{shapes,arrows}\n\\usetikzlibrary{positioning,quotes}\n\n\\renewcommand{\\familydefault}{\\sfdefault}\n\n\\begin{document}\n\\pagestyle{empty}\n\n\\definecolor{spikecolor}{RGB}{180,51,76}\n\\definecolor{pyblue}{HTML}{1f77b4}\n\\definecolor{pyorange}{HTML}{ff7f0e}\n\n\n% Define block styles\n\\tikzstyle{block} = [rectangle, draw, text width=3em, text centered, rounded\ncorners, minimum height=2.2em]\n\\tikzstyle{sum} = [block]\n\\tikzstyle{syn} = [block, fill=black!10 ]\n\\tikzstyle{mem} = [block, fill=pyblue!40 ]\n\\tikzstyle{spk} = [block, fill=pyorange!40 ]\n   \n\n\\begin{tikzpicture}[node distance = 1.2cm, auto]\n    \\pgfmathsetmacro{\\n}{4}  % sets number of units -1\n    \\pgfmathsetmacro{\\nm}{3}  % set to \\n -1\n    \\pgfmathsetmacro{\\sep}{2.6}  \n\n\t% Place nodes \n\t\\foreach \\t in {0,...,\\n} {\n\t\t\\node [sum] (x\\t) at (\\sep*\\t,0) {$S^{(0)}[\\t]$};\n\t\t\\node [syn, above of=x\\t] (I\\t) {$I^{(1)}[\\t]$};\n\t\t\\node [mem, above of=I\\t] (U\\t) {$U^{(1)}[\\t]$};\n\t\t\\node [spk, above of=U\\t] (S\\t) {$S^{(1)}[\\t]$};\n\t\t\\node [block, above of=S\\t] (y\\t) {};\n\t\t\\node [above of=y\\t] (downstream\\t) {};\n\t}\n\n\n    % Draw edges\n\t\\foreach \\t in {0,...,\\n} {\n    \t\\path [->, draw, thick] (U\\t) -- (S\\t);\n    \t% \\path [->, draw, thick] (y\\t) -- (downstream\\t) node[near start, right]\n\t\t% {$W^{(1,2)}$};\n\t\t% \\path [->, draw, thick, spikecolor] (upstream\\t) -- (x\\t) node[near end,\n\t\t% right] {$W^{(0,1)}$};\n\t}\n\n\t% Forward in time\n\t\\foreach \\t in {0,...,\\nm} {\n\t\t\\pgfmathtruncatemacro{\\next}{\\t + 1}\n\t\t\\path [->, draw, thick] (x\\t) -- (I\\next) node[near start, right]\n\t\t{~$W^{(1)}$};\n    \t\\path [->, draw, thick] (I\\t) -- (U\\next);\n\n    \t\\path [->, draw, thick] (I\\t) -- (I\\next) node[near end] {$\\alpha$};\n\t\t\\path [->, draw, thick] (U\\t) -- (U\\next) node[near end] {$\\beta$};\n\n\t\t\\path [->, draw, thick, pyorange] (S\\t) -- (y\\next) node[near end,\n\t\tleft] {$W^{(2)}$};\n\t\t\\path [->, draw, thick, pyorange, dashed] (S\\t) -- (U\\next) node[near start] {$-\\mathds{1}$};\n\t\t\\path [->, draw, thick, pyorange] (S\\t) -- (I\\next) node[near end,\n\t\tleft] {$V^{(1)}$};\n\n\t}\n\n\t\\node[node distance=1.0cm, below of=x0] (a0) {};\n\t\\node[right of=a0] (a1) {};\n\t% \\path [->, draw, thick, black] (a0) -- (a1) node[near start, below] {Time};\n\n\t\\draw  (a0)  edge[->,\"Time\"]  (a1) ;\n\n\\end{tikzpicture}\n\n\\end{document}\n"
  },
  {
    "path": "notebooks/figures/surrgrad/Makefile",
    "content": "all: surrgrad.png\n\n%.png: %.pdf\n\tconvert -density 150 $< $@\n\n%.pdf: %.tex\n\tpdflatex $< \n\n%.tex: %.gnu\n\tgnuplot $<\n"
  },
  {
    "path": "notebooks/figures/surrgrad/surrgrad.gnu",
    "content": "#!/usr/bin/gnuplot\n\nset border 3\nset xtics nomirror out\nset ytics nomirror out\n\n\ntheta(x) = x>0?1:0\n\nsigma(x) = 0.5*x/(abs(x)+1)+0.5\n\nset xlabel '$x$'\n# set ylabel 'Output'\nset key bottom right\n\nset xrange [-1:1]\nplot theta(x) lw 3 title '$\\Theta(x)$', \\\n     sigma(x) lw 3 title '$\\sigma(x)$', \\\n     sigma(10*x) lw 3 title '$\\sigma(10 x)$', \\\n\n\nset term epslatex standalone dashed color size 3.3, 2.0 font '\\sfdefault,8' \\\nheader '\\usepackage{sfmath}'\nset out 'surrgrad.tex'\nrep\n"
  },
  {
    "path": "notebooks/utils.py",
    "content": "import os\nimport urllib.request\nimport gzip, shutil\nimport hashlib\n\nfrom six.moves.urllib.error import HTTPError \nfrom six.moves.urllib.error import URLError\nfrom six.moves.urllib.request import urlretrieve\n\n# The functions used in this file to download the dataset are based on \n# code from the keras library. Specifically, from the following file:\n# https://github.com/tensorflow/tensorflow/blob/v2.3.1/tensorflow/python/keras/utils/data_utils.py\n\n\ndef get_shd_dataset(cache_dir, cache_subdir):\n\n    # The remote directory with the data files\n    base_url = \"https://zenkelab.org/datasets\"\n\n    # Retrieve MD5 hashes from remote\n    response = urllib.request.urlopen(\"%s/md5sums.txt\"%base_url)\n    data = response.read() \n    lines = data.decode('utf-8').split(\"\\n\")\n    file_hashes = { line.split()[1]:line.split()[0] for line in lines if len(line.split())==2 }\n\n    # Download the Spiking Heidelberg Digits (SHD) dataset\n    files = [ \"shd_train.h5.gz\", \n              \"shd_test.h5.gz\",\n            ]\n    for fn in files:\n        origin = \"%s/%s\"%(base_url,fn)\n        hdf5_file_path = get_and_gunzip(origin, fn, md5hash=file_hashes[fn], cache_dir=cache_dir, cache_subdir=cache_subdir)\n        # print(\"File %s decompressed to:\"%(fn))\n        print(\"Available at: %s\"%hdf5_file_path)\n\ndef get_and_gunzip(origin, filename, md5hash=None, cache_dir=None, cache_subdir=None):\n    gz_file_path = get_file(filename, origin, md5_hash=md5hash, cache_dir=cache_dir, cache_subdir=cache_subdir)\n    hdf5_file_path = gz_file_path[:-3]\n    if not os.path.isfile(hdf5_file_path) or os.path.getctime(gz_file_path) > os.path.getctime(hdf5_file_path):\n        print(\"Decompressing %s\"%gz_file_path)\n        with gzip.open(gz_file_path, 'r') as f_in, open(hdf5_file_path, 'wb') as f_out:\n            shutil.copyfileobj(f_in, f_out)\n    return hdf5_file_path\n\ndef validate_file(fpath, file_hash, algorithm='auto', chunk_size=65535):\n    if (algorithm == 'sha256') or (algorithm == 'auto' and len(file_hash) == 64):\n        hasher = 'sha256'\n    else:\n        hasher = 'md5'\n\n    if str(_hash_file(fpath, hasher, chunk_size)) == str(file_hash):\n        return True\n    else:\n        return False\n\ndef _hash_file(fpath, algorithm='sha256', chunk_size=65535):\n    if (algorithm == 'sha256') or (algorithm == 'auto' and len(hash) == 64):\n        hasher = hashlib.sha256()\n    else:\n        hasher = hashlib.md5()\n\n    with open(fpath, 'rb') as fpath_file:\n        for chunk in iter(lambda: fpath_file.read(chunk_size), b''):\n            hasher.update(chunk)\n\n    return hasher.hexdigest()\n\ndef get_file(fname,\n             origin,\n             md5_hash=None,\n             file_hash=None,\n             cache_subdir='datasets',\n             hash_algorithm='auto',\n             extract=False,\n             archive_format='auto',\n             cache_dir=None):\n    if cache_dir is None:\n        cache_dir = os.path.join(os.path.expanduser('~'), '.data-cache')\n    if md5_hash is not None and file_hash is None:\n        file_hash = md5_hash\n        hash_algorithm = 'md5'\n    datadir_base = os.path.expanduser(cache_dir)\n    if not os.access(datadir_base, os.W_OK):\n        datadir_base = os.path.join('/tmp', '.data-cache')\n    datadir = os.path.join(datadir_base, cache_subdir)\n\n    # Create directories if they don't exist\n    os.makedirs(cache_dir, exist_ok=True)\n    os.makedirs(datadir, exist_ok=True)\n\n    fpath = os.path.join(datadir, fname)\n\n    download = False\n    if os.path.exists(fpath):\n    # File found; verify integrity if a hash was provided.\n        if file_hash is not None:\n            if not validate_file(fpath, file_hash, algorithm=hash_algorithm):\n                print('A local file was found, but it seems to be '\n                      'incomplete or outdated because the ' + hash_algorithm +\n                      ' file hash does not match the original value of ' + file_hash +\n                      ' so we will re-download the data.')\n                download = True\n    else:\n        download = True\n\n    if download:\n        print('Downloading data from', origin)\n\n        error_msg = 'URL fetch failure on {}: {} -- {}'\n        try:\n            try:\n                urlretrieve(origin, fpath)\n            except HTTPError as e:\n                raise Exception(error_msg.format(origin, e.code, e.msg))\n            except URLError as e:\n                raise Exception(error_msg.format(origin, e.errno, e.reason))\n        except (Exception, KeyboardInterrupt) as e:\n            if os.path.exists(fpath):\n                os.remove(fpath)\n\n    return fpath\n"
  },
  {
    "path": "requirements.txt",
    "content": "jupyter==1.0.0\njupyter-client==6.1.6\njupyter-console==6.1.0\njupyter-core==4.6.3\ntorch==1.6.0\ntorchvision==0.7.0\nseaborn==0.10.1\nh5py==2.10.0\n"
  }
]