Spiking Neural Networks with PyTorch.ipynb

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    "# Spiking Neural Network (SNN) with PyTorch : towards bridging the gap between deep learning and the human brain\n",
    "\n",
    "> I think I've discovered something amazing: Hebbian learning naturally takes place during the backpropagation of SNNs. Backpropagation in Spiking Neural Networks (SNNs) engenders Spike-Timing-Dependent Plasticity (STDP)-like Hebbian learning behavior.\n",
    "\n",
    "\n",
    "So:\n",
    "- At first I simply thought \"hey, what about coding a Spiking Neural Network using an automatic differentiation framework?\" Here it is. \n",
    "- Then I started reading on how to achieve that, such as reading on [Hebbian learning](https://en.wikipedia.org/wiki/Hebbian_theory). Quickly explained: Hebbian learning is somehow the saying that \"neurons that fire together, wire together\". \n",
    "- Then, I think [I've discovered something amazing](https://www.youtube.com/watch?v=Jo6dkHgT6TI). What if when doing backpropagation on a Spiking Neural Network (SNN), Hebbian learning would take place naturally as a side effect of adding that refractory time axis?\n",
    "- I had the opportunity to discuss that idea with Yoshua Bengio at a conference, and I couldn't get the idea out of my head past that point, so I coded it as follow.\n",
    "- As a conclusion, I think that the link between deep learning and the human brain is closer than we might think: backpropagation is akin to Hebbian learning. \n",
    "\n",
    "If you don't understand what SNNs are, you should watch [this interesting SNN animation](https://www.youtube.com/embed/3JQ3hYko51Y?start=120) which will quickly get you a feel of what it is (especially notice how neurons gets activated only gradually over time like a storm rather than statically from their inputs).\n",
    "\n",
    "<!-- <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/3JQ3hYko51Y?start=120\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe> -->\n",
    "\n",
    "\n",
    "## Spiking Neural Networks (SNNs) v.s. Artificial Neural Networks (ANNs)\n",
    "\n",
    "In SNNs, there is a time axis and the neural network sees data througout time, and activation functions are isntead spikes that are raised past a certain pre-activation threshold. Pre-activation values constantly fades if neurons aren't excited enough. Think of it like a time-distributed ReLU with spikes or nothing at certain time steps. \n",
    "\n",
    "Spiking Neural Networks (SNNs) are neural networks that are closer to what happens in the brain compared to what people usually code when doing Machine Learning and Deep Learning. In the case of SNNs, the neurons accumulate the input activation until a threshold is reached, and when this threshold is reached, the neuron empties itself from it's activation and fire. Once empty, it should indeed take some [refractory period](https://en.wikipedia.org/wiki/Refractory_period_(physiology)) until it fires again, as it happen in the brain. \n",
    "\n",
    "This implies adding a **time axis** to Artificial Neural Networks (ANNs), where signal is accumulated throughout time in a pre-activation phase, then once a threshold is reached, signal is raised to the neurons above as a firing activation. At every moment, such as when the threshold isn't reached yet, the signal's pre-activation value fades. \n",
    "\n",
    "So I roughly replicated this refractory neuron firing pattern behavior here with PyTorch. I coded this without reading existing code for me to try come up with a solution by myself as some kind of challenge and also not to be biased by what others do, building from first principles. I also supposed that throughout time, perceptrons' readings would be noisy to add randomization. As an afterthought, I'm satisfied with what I came up with. \n",
    "\n",
    "## Spiking Neural Networks (SNNs) v.s. Recurrent Neural Networks (RNNs)\n",
    "\n",
    "The SNN is NOT an [RNN](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition), despite it evolves through time too. For this SNN to be an RNN, I believe it would require some more connections such as from the outputs back into the inputs. In fact, RNNs are defined as a function of some inputs and of many neurons at the previous time step, such as:\n",
    "\n",
    "<a href=\"https://www.codecogs.com/eqnedit.php?latex=$o_t&space;=&space;f(o_{t-1},&space;x_t)$\" target=\"_blank\"><img src=\"https://latex.codecogs.com/gif.latex?$o_t&space;=&space;f(o_{t-1},&space;x_t)$\" title=\"$o_t = f(o_{t-1}, x_t)$\" /></a> \n",
    "\n",
    "for example. In our case, we keep some state, but it's nothing comparable to having a connection back to other neurons in the past. Forgive me in advance for having appended the suffix \"RNN\" to my SNN PyTorch class below, as I use it like an RNN with a time axis. But the concept is theoretically different despite they share a common \"forward time axis\" structure. \n",
    "\n",
    "\n",
    "## How does it works?\n",
    "\n",
    "Okay, let's dive into the details. \n",
    "\n",
    "The way I define a neuron's firing method is through the following steps, where the argument `x` is an input:\n",
    "\n",
    "- Before anything, we need an initialize (or to empty) the state for each neuron upon starting predictions. \n",
    "```python\n",
    "    self.prev_inner = torch.zeros([batch_size, self.n_hidden]).to(self.device)\n",
    "    self.prev_outer = torch.zeros([batch_size, self.n_hidden]).to(self.device)\n",
    "```\n",
    "- Then, a weight matrix multiplies the input x, which in our case is the [handwritten MNIST digits](http://yann.lecun.com/exdb/mnist/). It is to be noted that x was modified to be flickering randomly through time (depending on the intensity of the original input x multiplied by a random uniform noise mask), or else x is already the output of a lower deep spiking layer: \n",
    "```python\n",
    "    input_excitation = self.fully_connected(x)\n",
    "```\n",
    "- I then add the result to a decayed version of the information inside the neuron that we already had at the previous time step / time tick (Δt time elapsed). The `decay_multiplier` serves the purpose of slowly fading the inner activation such that we don't accumulate stimulis for too long to be able to have the neurons to rest. The `decay_multiplier` could have a value of 0.9 for example. Decay as such is also called exponential decay and yields an effect of [Exponential moving average](https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average) over time on the most recent values seen, which also affects the gradients upon backpropagating. So by repeatedly multplying by 0.9 the inner activation through time, it decays and neurons unexcite themselves before firing. In this sense, it's now really true that \"neurons that fire together wire together\": when a pre-synaptic input is received closer to the moment of giving an output, that recentmost value will not have had the time to be decayed/faded. This way, the gradient of recent neurons that participated in exciting the current neuron that fired will be strong. Learning will be able to take place by gradient descent according to the decay's weightings. So in the opposite case, a stimuli that happened too long ago will suffer from vanishing gradients since it has been exponentially decayed down. So it won't be useful in the learning process of backprop, which is what we want and respects the \"neurons that fire together, wire together\" idiom of Hebbian learning. \n",
    "```python\n",
    "    inner_excitation = input_excitation + self.prev_inner * self.decay_multiplier\n",
    "```\n",
    "- Now, we compute the activation of the neurons to find their output value. We have a threshold to reach before having the neuron activating. The ReLU function might not be the most appropriate here (more on that later), but I wanted to get a working prototype fast: \n",
    "```python\n",
    "    outer_excitation = F.relu(inner_excitation - self.threshold)\n",
    "```\n",
    "- Now, magic happens. If the neuron fires, the activation of the neuron is *subtracted to its inner state* to reset each neuron. First, this has the effect of resetting them to a resting position such that they won't be firing constantly upon being activated once. Second, resetting them as such will clip the gradient through time to isolate each firing events from each other. SNNs as such are truly inspired from the brain, as the natural neurons also have a refractory period: neurons, once they fire, will need to wait a bit before firing again even if fully excited by lower neurons they have as inputs. So here, I even subtracted a second penalty named `penalty_threshold` after each resetting `threshold`. Disclaimer: I wasn't sure whether the negative part in the biological refractory period was on the outputs of the neurons or inside the neurons (e.g.: axon v.s. body?), so here I've simply put it inside. Let's see how I subtract this just when the neuron fires to have it to have a refractory period: \n",
    "```python\n",
    "    do_penalize_gate = (outer_excitation > 0).float()\n",
    "    inner_excitation = inner_excitation - (self.penalty_threshold + outer_excitation) * do_penalize_gate\n",
    "```\n",
    "- Finally, I return the previous output, simulating a small firing delay, which is useless for now, but which may be interesting to have if the SNN I coded was ever to have Recurrent connections which would require time offsets in the connections from top layers near the outputs back into bottom layers near the input: \n",
    "```python\n",
    "    delayed_return_state = self.prev_inner\n",
    "    delayed_return_output = self.prev_outer\n",
    "    self.prev_inner = inner_excitation\n",
    "    self.prev_outer = outer_excitation\n",
    "    return delayed_return_state, delayed_return_output\n",
    "```\n",
    "- Past that, to do the classification, the values of the classification output spiking neurons are averaged over the time axis so as to have one number per class to plug into the softmax cross entropy loss for classification as we know it and we backpropagate. This means the present SNN PyTorch class is reusable within any other feedforward neural network, as it repeats intputs over time with random noisy masks, and averages outputs over time.  \n",
    "\n",
    "Amazingly, it worked on the 1st try once the dimension mismatching errors were fixed. And the accuracy was about the same of the accuracy of a simple non-spiking Feedforward Neural Network with the same number of neurons. And I didn't even tuned the threshold. In the end, I realized that coding and training a Spiking Neural Network (SNN) with PyTorch was easy enough as shown above, it can be coded in an evening as such. \n",
    "\n",
    "\n",
    "\n",
    "Basically, the neurons' activation must decay through time and fire only when getting past a certain threshold. So I've gated the output of the \n",
    "\n",
    "\n",
    "## Results\n",
    "\n",
    "Scroll on! Nice visuals awaits.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torchvision.datasets\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision.transforms as transforms\n",
    "from torch.autograd import Variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def train(model, device, train_set_loader, optimizer, epoch, logging_interval=100):\n",
    "    # This method is derived from: \n",
    "    # https://github.com/pytorch/examples/blob/master/mnist/main.py\n",
    "    # Was licensed BSD-3-clause\n",
    "    \n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_set_loader):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.nll_loss(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        if batch_idx % logging_interval == 0:\n",
    "            pred = output.max(1, keepdim=True)[1]  # get the index of the max log-probability\n",
    "            correct = pred.eq(target.view_as(pred)).float().mean().item()\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)] Loss: {:.6f} Accuracy: {:.2f}%'.format(\n",
    "                epoch, batch_idx * len(data), len(train_set_loader.dataset),\n",
    "                100. * batch_idx / len(train_set_loader), loss.item(),\n",
    "                100. * correct))\n",
    "\n",
    "def train_many_epochs(model): \n",
    "    epoch = 1\n",
    "    optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.5)\n",
    "    train(model, device, train_set_loader, optimizer, epoch, logging_interval=10)\n",
    "    test(model, device, test_set_loader)\n",
    "\n",
    "    epoch = 2\n",
    "    optimizer = optim.SGD(model.parameters(), lr=0.05, momentum=0.5)\n",
    "    train(model, device, train_set_loader, optimizer, epoch, logging_interval=10)\n",
    "    test(model, device, test_set_loader)\n",
    "\n",
    "    epoch = 3\n",
    "    optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)\n",
    "    train(model, device, train_set_loader, optimizer, epoch, logging_interval=10)\n",
    "    test(model, device, test_set_loader)\n",
    "            \n",
    "def test(model, device, test_set_loader):\n",
    "    # This method is derived from: \n",
    "    # https://github.com/pytorch/examples/blob/master/mnist/main.py\n",
    "    # Was licensed BSD-3-clause\n",
    "    \n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for data, target in test_set_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            # Note: with `reduce=True`, I'm not sure what would happen with a final batch size \n",
    "            # that would be smaller than regular previous batch sizes. For now it works.\n",
    "            test_loss += F.nll_loss(output, target, reduce=True).item() # sum up batch loss\n",
    "            pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "\n",
    "    test_loss /= len(test_set_loader.dataset)\n",
    "    print(\"\")\n",
    "    print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'.format(\n",
    "        test_loss, \n",
    "        correct, len(test_set_loader.dataset),\n",
    "        100. * correct / len(test_set_loader.dataset)))\n",
    "    print(\"\")\n",
    "\n",
    "def download_mnist(data_path):\n",
    "    if not os.path.exists(data_path):\n",
    "        os.mkdir(data_path)\n",
    "    transformation = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])\n",
    "    training_set = torchvision.datasets.MNIST(data_path, train=True, transform=transformation, download=True)\n",
    "    testing_set = torchvision.datasets.MNIST(data_path, train=False, transform=transformation, download=True)\n",
    "    return training_set, testing_set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 1000\n",
    "DATA_PATH = './data'\n",
    "\n",
    "training_set, testing_set = download_mnist(DATA_PATH)\n",
    "train_set_loader = torch.utils.data.DataLoader(\n",
    "    dataset=training_set,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True)\n",
    "test_set_loader = torch.utils.data.DataLoader(\n",
    "    dataset=testing_set,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use GPU whever possible!\n",
    "use_cuda = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda\" if use_cuda else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SpikingNeuronLayerRNN(nn.Module):\n",
    "    \n",
    "    def __init__(self, device, n_inputs=28*28, n_hidden=100, decay_multiplier=0.9, threshold=2.0, penalty_threshold=2.5):\n",
    "        super(SpikingNeuronLayerRNN, self).__init__()\n",
    "        self.device = device\n",
    "        self.n_inputs = n_inputs\n",
    "        self.n_hidden = n_hidden\n",
    "        self.decay_multiplier = decay_multiplier\n",
    "        self.threshold = threshold\n",
    "        self.penalty_threshold = penalty_threshold\n",
    "        \n",
    "        self.fc = nn.Linear(n_inputs, n_hidden)\n",
    "        \n",
    "        self.init_parameters()\n",
    "        self.reset_state()\n",
    "        self.to(self.device)\n",
    "        \n",
    "    def init_parameters(self):\n",
    "        for param in self.parameters():\n",
    "            if param.dim() >= 2:\n",
    "                nn.init.xavier_uniform_(param)\n",
    "        \n",
    "    def reset_state(self):\n",
    "        self.prev_inner = torch.zeros([self.n_hidden]).to(self.device)\n",
    "        self.prev_outer = torch.zeros([self.n_hidden]).to(self.device)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Call the neuron at every time step.\n",
    "        \n",
    "        x: activated_neurons_below\n",
    "        \n",
    "        return: a tuple of (state, output) for each time step. Each item in the tuple\n",
    "        are then themselves of shape (batch_size, n_hidden) and are PyTorch objects, such \n",
    "        that the whole returned would be of shape (2, batch_size, n_hidden) if casted.\n",
    "        \"\"\"\n",
    "        if self.prev_inner.dim() == 1:\n",
    "            # Adding batch_size dimension directly after doing a `self.reset_state()`:\n",
    "            batch_size = x.shape[0]\n",
    "            self.prev_inner = torch.stack(batch_size * [self.prev_inner])\n",
    "            self.prev_outer = torch.stack(batch_size * [self.prev_outer])\n",
    "        \n",
    "        # 1. Weight matrix multiplies the input x\n",
    "        input_excitation = self.fc(x)\n",
    "        \n",
    "        # 2. We add the result to a decayed version of the information we already had.\n",
    "        inner_excitation = input_excitation + self.prev_inner * self.decay_multiplier\n",
    "        \n",
    "        # 3. We compute the activation of the neuron to find its output value, \n",
    "        #    but before the activation, there is also a negative bias that refrain thing from firing too much.\n",
    "        outer_excitation = F.relu(inner_excitation - self.threshold)\n",
    "        \n",
    "        # 4. If the neuron fires, the activation of the neuron is subtracted to its inner state \n",
    "        #    (and with an extra penalty for increase refractory time), \n",
    "        #    because it discharges naturally so it shouldn't fire twice. \n",
    "        do_penalize_gate = (outer_excitation > 0).float()\n",
    "        # TODO: remove following /2?\n",
    "        inner_excitation = inner_excitation - (self.penalty_threshold/self.threshold * inner_excitation) * do_penalize_gate\n",
    "        \n",
    "        # 5. The outer excitation has a negative part after the positive part. \n",
    "        outer_excitation = outer_excitation #+ torch.abs(self.prev_outer) * self.decay_multiplier / 2.0\n",
    "        \n",
    "        # 6. Setting internal values before returning. \n",
    "        #    And the returning value is the one of the previous time step to delay \n",
    "        #    activation of 1 time step of \"processing\" time. For logits, we don't take activation.\n",
    "        delayed_return_state = self.prev_inner\n",
    "        delayed_return_output = self.prev_outer\n",
    "        self.prev_inner = inner_excitation\n",
    "        self.prev_outer = outer_excitation\n",
    "        return delayed_return_state, delayed_return_output\n",
    "\n",
    "\n",
    "class InputDataToSpikingPerceptronLayer(nn.Module):\n",
    "    \n",
    "    def __init__(self, device):\n",
    "        super(InputDataToSpikingPerceptronLayer, self).__init__()\n",
    "        self.device = device\n",
    "        \n",
    "        self.reset_state()\n",
    "        self.to(self.device)\n",
    "        \n",
    "    def reset_state(self):\n",
    "        #     self.prev_state = torch.zeros([self.n_hidden]).to(self.device)\n",
    "        pass\n",
    "    \n",
    "    def forward(self, x, is_2D=True):\n",
    "        x = x.view(x.size(0), -1)  # Flatten 2D image to 1D for FC\n",
    "        random_activation_perceptron = torch.rand(x.shape).to(self.device)\n",
    "        return random_activation_perceptron * x\n",
    "\n",
    "\n",
    "class OutputDataToSpikingPerceptronLayer(nn.Module):\n",
    "    \n",
    "    def __init__(self, average_output=True):\n",
    "        \"\"\"\n",
    "        average_output: might be needed if this is used within a regular neural net as a layer.\n",
    "        Otherwise, sum may be numerically more stable for gradients with setting average_output=False.\n",
    "        \"\"\"\n",
    "        super(OutputDataToSpikingPerceptronLayer, self).__init__()\n",
    "        if average_output:\n",
    "            self.reducer = lambda x, dim: x.sum(dim=dim)\n",
    "        else:\n",
    "            self.reducer = lambda x, dim: x.mean(dim=dim)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        if type(x) == list:\n",
    "            x = torch.stack(x)\n",
    "        return self.reducer(x, 0)\n",
    "\n",
    "\n",
    "class SpikingNet(nn.Module):\n",
    "    \n",
    "    def __init__(self, device, n_time_steps, begin_eval):\n",
    "        super(SpikingNet, self).__init__()\n",
    "        assert (0 <= begin_eval and begin_eval < n_time_steps)\n",
    "        self.device = device\n",
    "        self.n_time_steps = n_time_steps\n",
    "        self.begin_eval = begin_eval\n",
    "        \n",
    "        self.input_conversion = InputDataToSpikingPerceptronLayer(device)\n",
    "        \n",
    "        self.layer1 = SpikingNeuronLayerRNN(\n",
    "            device, n_inputs=28*28, n_hidden=100,\n",
    "            decay_multiplier=0.9, threshold=1.0, penalty_threshold=1.5\n",
    "        )\n",
    "        \n",
    "        self.layer2 = SpikingNeuronLayerRNN(\n",
    "            device, n_inputs=100, n_hidden=10,\n",
    "            decay_multiplier=0.9, threshold=1.0, penalty_threshold=1.5\n",
    "        )\n",
    "        \n",
    "        self.output_conversion = OutputDataToSpikingPerceptronLayer(average_output=False)  # Sum on outputs.\n",
    "        \n",
    "        self.to(self.device)\n",
    "    \n",
    "    def forward_through_time(self, x):\n",
    "        \"\"\"\n",
    "        This acts as a layer. Its input is non-time-related, and its output too.\n",
    "        So the time iterations happens inside, and the returned layer is thus\n",
    "        passed through global average pooling on the time axis before the return \n",
    "        such as to be able to mix this pipeline with regular backprop layers such\n",
    "        as the input data and the output data.\n",
    "        \"\"\"\n",
    "        self.input_conversion.reset_state()\n",
    "        self.layer1.reset_state()\n",
    "        self.layer2.reset_state()\n",
    "\n",
    "        out = []\n",
    "        \n",
    "        all_layer1_states = []\n",
    "        all_layer1_outputs = []\n",
    "        all_layer2_states = []\n",
    "        all_layer2_outputs = []\n",
    "        for _ in range(self.n_time_steps):\n",
    "            xi = self.input_conversion(x)\n",
    "            \n",
    "            # For layer 1, we take the regular output.\n",
    "            layer1_state, layer1_output = self.layer1(xi)\n",
    "            \n",
    "            # We take inner state of layer 2 because it's pre-activation and thus acts as out logits.\n",
    "            layer2_state, layer2_output = self.layer2(layer1_output)\n",
    "            \n",
    "            all_layer1_states.append(layer1_state)\n",
    "            all_layer1_outputs.append(layer1_output)\n",
    "            all_layer2_states.append(layer2_state)\n",
    "            all_layer2_outputs.append(layer2_output)\n",
    "            out.append(layer2_state)\n",
    "            \n",
    "        out = self.output_conversion(out[self.begin_eval:])\n",
    "        return out, [[all_layer1_states, all_layer1_outputs], [all_layer2_states, all_layer2_outputs]]\n",
    "    \n",
    "    def forward(self, x):\n",
    "        out, _ = self.forward_through_time(x)\n",
    "        return F.log_softmax(out, dim=-1)\n",
    "\n",
    "    def visualize_all_neurons(self, x):\n",
    "        assert x.shape[0] == 1 and len(x.shape) == 4, (\n",
    "            \"Pass only 1 example to SpikingNet.visualize(x) with outer dimension shape of 1.\")\n",
    "        _, layers_state = self.forward_through_time(x)\n",
    "\n",
    "        for i, (all_layer_states, all_layer_outputs) in enumerate(layers_state):\n",
    "            layer_state  =  torch.stack(all_layer_states).data.cpu().numpy().squeeze().transpose()\n",
    "            layer_output = torch.stack(all_layer_outputs).data.cpu().numpy().squeeze().transpose()\n",
    "            \n",
    "            self.plot_layer(layer_state, title=\"Inner state values of neurons for layer {}\".format(i))\n",
    "            self.plot_layer(layer_output, title=\"Output spikes (activation) values of neurons for layer {}\".format(i))\n",
    "    \n",
    "    def visualize_neuron(self, x, layer_idx, neuron_idx):\n",
    "        assert x.shape[0] == 1 and len(x.shape) == 4, (\n",
    "            \"Pass only 1 example to SpikingNet.visualize(x) with outer dimension shape of 1.\")\n",
    "        _, layers_state = self.forward_through_time(x)\n",
    "\n",
    "        all_layer_states, all_layer_outputs = layers_state[layer_idx]\n",
    "        layer_state  =  torch.stack(all_layer_states).data.cpu().numpy().squeeze().transpose()\n",
    "        layer_output = torch.stack(all_layer_outputs).data.cpu().numpy().squeeze().transpose()\n",
    "\n",
    "        self.plot_neuron(layer_state[neuron_idx], title=\"Inner state values neuron {} of layer {}\".format(neuron_idx, layer_idx))\n",
    "        self.plot_neuron(layer_output[neuron_idx], title=\"Output spikes (activation) values of neuron {} of layer {}\".format(neuron_idx, layer_idx))\n",
    "\n",
    "    def plot_layer(self, layer_values, title):\n",
    "        \"\"\"\n",
    "        This function is derived from: \n",
    "            https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition \n",
    "        Which was released under the MIT License. \n",
    "        \"\"\"\n",
    "        width = max(16, layer_values.shape[0] / 8)\n",
    "        height = max(4, layer_values.shape[1] / 8)\n",
    "        plt.figure(figsize=(width, height))\n",
    "        plt.imshow(\n",
    "            layer_values,\n",
    "            interpolation=\"nearest\",\n",
    "            cmap=plt.cm.rainbow\n",
    "        )\n",
    "        plt.title(title)\n",
    "        plt.colorbar()\n",
    "        plt.xlabel(\"Time\")\n",
    "        plt.ylabel(\"Neurons of layer\")\n",
    "        plt.show()\n",
    "\n",
    "    def plot_neuron(self, neuron_through_time, title):\n",
    "        width = max(16, len(neuron_through_time) / 8)\n",
    "        height = 4\n",
    "        plt.figure(figsize=(width, height))\n",
    "        plt.title(title)\n",
    "        plt.plot(neuron_through_time)\n",
    "        plt.xlabel(\"Time\")\n",
    "        plt.ylabel(\"Neuron's activation\")\n",
    "        plt.show()\n",
    "\n",
    "class NonSpikingNet(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(NonSpikingNet, self).__init__()\n",
    "        self.layer1 = nn.Linear(28*28, 100)\n",
    "        self.layer2 = nn.Linear(100, 10)\n",
    "\n",
    "    def forward(self, x, is_2D=True):\n",
    "        x = x.view(x.size(0), -1)  # Flatten 2D image to 1D for FC\n",
    "        x = F.relu(self.layer1(x))\n",
    "        x =        self.layer2(x)\n",
    "        return F.log_softmax(x, dim=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Spiking Neural Network (SNN)\n",
    "\n",
    "Let's use our `SpikingNet`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/60000 (0%)] Loss: 2.460052 Accuracy: 9.90%\n",
      "Train Epoch: 1 [10000/60000 (17%)] Loss: 1.811235 Accuracy: 30.00%\n",
      "Train Epoch: 1 [20000/60000 (33%)] Loss: 1.797833 Accuracy: 38.60%\n",
      "Train Epoch: 1 [30000/60000 (50%)] Loss: 0.645438 Accuracy: 83.30%\n",
      "Train Epoch: 1 [40000/60000 (67%)] Loss: 0.522837 Accuracy: 83.50%\n",
      "Train Epoch: 1 [50000/60000 (83%)] Loss: 0.528960 Accuracy: 81.80%\n",
      "\n",
      "Test set: Average loss: 0.0004, Accuracy: 8955/10000 (89.55%)\n",
      "\n",
      "Train Epoch: 2 [0/60000 (0%)] Loss: 0.405339 Accuracy: 87.80%\n",
      "Train Epoch: 2 [10000/60000 (17%)] Loss: 0.357420 Accuracy: 88.80%\n",
      "Train Epoch: 2 [20000/60000 (33%)] Loss: 0.326266 Accuracy: 90.10%\n",
      "Train Epoch: 2 [30000/60000 (50%)] Loss: 0.377100 Accuracy: 89.60%\n",
      "Train Epoch: 2 [40000/60000 (67%)] Loss: 0.335625 Accuracy: 90.60%\n",
      "Train Epoch: 2 [50000/60000 (83%)] Loss: 0.359532 Accuracy: 88.90%\n",
      "\n",
      "Test set: Average loss: 0.0003, Accuracy: 9061/10000 (90.61%)\n",
      "\n",
      "Train Epoch: 3 [0/60000 (0%)] Loss: 0.342230 Accuracy: 90.40%\n",
      "Train Epoch: 3 [10000/60000 (17%)] Loss: 0.347210 Accuracy: 89.90%\n",
      "Train Epoch: 3 [20000/60000 (33%)] Loss: 0.346477 Accuracy: 89.60%\n",
      "Train Epoch: 3 [30000/60000 (50%)] Loss: 0.317255 Accuracy: 90.70%\n",
      "Train Epoch: 3 [40000/60000 (67%)] Loss: 0.329143 Accuracy: 90.40%\n",
      "Train Epoch: 3 [50000/60000 (83%)] Loss: 0.310708 Accuracy: 90.70%\n",
      "\n",
      "Test set: Average loss: 0.0003, Accuracy: 9065/10000 (90.65%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spiking_model = SpikingNet(device, n_time_steps=128, begin_eval=0)\n",
    "train_many_epochs(spiking_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Feedforward Neural Network (for comparison)\n",
    "\n",
    "It has the same number of layers and neurons, and also uses ReLU activation, but it's not an SNN, this one is a regular one as defined in the code above with this other class `NonSpikingNet`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/60000 (0%)] Loss: 2.300953 Accuracy: 9.50%\n",
      "Train Epoch: 1 [10000/60000 (17%)] Loss: 1.908515 Accuracy: 62.40%\n",
      "Train Epoch: 1 [20000/60000 (33%)] Loss: 1.259780 Accuracy: 72.20%\n",
      "Train Epoch: 1 [30000/60000 (50%)] Loss: 0.861031 Accuracy: 83.00%\n",
      "Train Epoch: 1 [40000/60000 (67%)] Loss: 0.652988 Accuracy: 85.40%\n",
      "Train Epoch: 1 [50000/60000 (83%)] Loss: 0.609710 Accuracy: 84.40%\n",
      "\n",
      "Test set: Average loss: 0.0005, Accuracy: 8691/10000 (86.91%)\n",
      "\n",
      "Train Epoch: 2 [0/60000 (0%)] Loss: 0.469882 Accuracy: 88.30%\n",
      "Train Epoch: 2 [10000/60000 (17%)] Loss: 0.479579 Accuracy: 85.80%\n",
      "Train Epoch: 2 [20000/60000 (33%)] Loss: 0.466115 Accuracy: 88.20%\n",
      "Train Epoch: 2 [30000/60000 (50%)] Loss: 0.479764 Accuracy: 87.10%\n",
      "Train Epoch: 2 [40000/60000 (67%)] Loss: 0.472486 Accuracy: 85.50%\n",
      "Train Epoch: 2 [50000/60000 (83%)] Loss: 0.443070 Accuracy: 88.20%\n",
      "\n",
      "Test set: Average loss: 0.0004, Accuracy: 8880/10000 (88.80%)\n",
      "\n",
      "Train Epoch: 3 [0/60000 (0%)] Loss: 0.432652 Accuracy: 88.20%\n",
      "Train Epoch: 3 [10000/60000 (17%)] Loss: 0.472320 Accuracy: 86.80%\n",
      "Train Epoch: 3 [20000/60000 (33%)] Loss: 0.443402 Accuracy: 88.60%\n",
      "Train Epoch: 3 [30000/60000 (50%)] Loss: 0.401267 Accuracy: 90.00%\n",
      "Train Epoch: 3 [40000/60000 (67%)] Loss: 0.428927 Accuracy: 88.40%\n",
      "Train Epoch: 3 [50000/60000 (83%)] Loss: 0.383301 Accuracy: 90.10%\n",
      "\n",
      "Test set: Average loss: 0.0004, Accuracy: 8897/10000 (88.97%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "non_spiking_model = NonSpikingNet().to(device)\n",
    "train_many_epochs(non_spiking_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's see how the neurons spiked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tWsxstZndY2Y1BXRkgwQAAABwous0s9VjHl/r7tcefmBmd0haHDzvT8Y+cHc3My84xqnuvtXMVkq608x+5O5Pj1cpgjUAAAAA5TJptKHutWDPpm53X1X0S3d/edHvzGyHmS1x921mtkTSzoJ9bM3/3WBmd0l6rqRxgzWmQQIAAADAsbtJ0lvz/79V0teP3MDM5pnZzPz/nZJ+TtITE+2YYA0AAAAAjt2HJb3CzJ6S9PL8scxslZl9Kt/mXEmrzewRSd+W9GF3nzBYYxokAAAAgFK5pFE7McaF3H23pJcF5aslvT3//w8kPedo931itCAAAAAATDMEawAAAABQQUyDBAAAAFC6aZYNspIYWQMAAACACiJYAwAAAIAKIlgDAAAAgApizRoAAACAcplp1FizVi9G1gAAAACgggjWAAAAAKCCmAYJAAAAoFQuaaSBcaF60YIAAAAAUEEEawAAAABQQQRrAAAAAFBBrFkDAAAAUDpS99ePkTUAAAAAqCCCNQAAAACoIKZBAgAAACiVi2mQZWBkDQAAAAAqiGANAAAAACqIaZAAAAAAymUmb2BcqF60IAAAAABUEMEaAAAAAFQQwRoAAAAAVBBr1gAAAACUjtT99WNkDQAAAAAqiGANAAAAACqIaZAAAAAASuViGmQZGFkDAAAAgAoiWAMAAACACiJYAwAAAIAKYs0aAAAAgNKxZq1+jKwBAAAAQAURrAEAAABABTENEgAAAECp3EyjxrhQvWhBAAAAAKgggjUAAAAAqCCmQQIAAAAoHdkg68fIGgAAAABUEMEaAAAAAFQQwRoAAAAAVBBr1gAAAACUyiWNNLBmrV6MrAEAAABABRGsAQAAAEAFMQ0SAAAAQLnMNGqMC9WLFgQAAACACiJYAwAAAIAKIlgDAAAAgApizRoAAACA0rmRur9ejKwBAAAAQAURrAEAAABABTENEgAAAECpXNKomAZZL0bWAAAAAKCCCNYAAAAAoIKYBgkAAACgdKNkg6wbI2sAAAAAUEEEawAAAABQQQRrAAAAAFBBrFkDAAAAUDLTqDEuVC9aEAAAAAAqiGANAAAAACqIaZAAAAAASuUidX8ZGFkDAAAAgAoiWAMAAACACiJYAwAAAIAKYs0aAAAAgHKZNMKatboxsgYAAAAAFUSwBgAAAAAVxDRIAAAAAKUidX85GFkDAAAAgAoiWAMAAACACmIaJAAAAICSmUYZF6obLQgAAAAAFUSwBgAAAAAVRLAGAAAAABXEmjUAAAAApXNS99eNkTUAAAAAqCCCNQAAAACoIKZBAgAAACiVSxplGmTdGFkDAAAAgAoiWAMAAACACiJYAwAAAIAKYs0aAAAAgNKNijVr9WJkDQAAAAAqiGANAAAAACqIaZAAAAAASuUyjRrjQvWiBQEAAACgggjWAAAAAKCCmAYJAAAAoHRkg6wfI2sAAAAAUEEEawAAAABQQQRrAAAAAHCMzOz1Zva4mY2a2apxtrvCzNaZ2Xoze38t+yZYA4AKMLPPmdlfT3U9DjOzRWb2XTPbZ2Yfner6PBvM7Gwzezh/ze8qYX9/YWb/VkbdAGC6cZNGzabNT50ek/Q6Sd8t2sDMGiV9QtKVks6T9EYzO2+iHROsATgmZrbRzF4+1fUYz9F+WDazXzCzLZNZp2nkGkndktrd/T1TXZlnyXslfdvdZ7v7P051ZcpiZs1m9pX8nnUz+4WprhMAHE/cfY27r5tgs+dLWu/uG9x9SNL1kq6aaN8EawCmDcvQbz07TpX0hLv7VFbCzJ7NrMWnSnr8WJ74LNez0Dj1uFvSr0va/ixWBwDwU8skbR7zeEteNq5xP/SYWaOZ/XudFQNwnDOz3zSzu83s781sj5n92MyuHPP7u8zsr8zs+/kUs9vMrHPM719gZj8ws14ze2TsX/7z5/6NmX1f0oCklcHx32dmW/N9rzOzl5nZFZL+WNKvmVm/mT2Sb/tbZrYm33aDmf23vHyWpP8naWm+fb+ZLTWzBjN7v5k9bWa7zexLZtZR0A5rzOzVYx43mdkuM7skf/xlM9tuZnvzKYbnj9eeR5S5mZ2R/39m3tabzGyHmf2zmZ2U/67TzL6Rt2WPmX2vKMA1sxeZ2f15fe43sxfl5Z+T9FZJ783bIRlBzadtfsLMvpm35b1mdvqY359jZrfndVhnZm8Y87u7zOztRa83f63vMLOnJD01Xl3H7C+8vsysxcz+LT93vflzFwWv505JL5H0T/lrPsvM5pjZ5/Nz+IyZ/enhtszr/H0z+5iZ7Zb0F1EbH3GM8Pyb2fPy89g4ZtvXjblmC69BM1uRt9fbzGyTpDuPPK67D7n7P7j73ZJGJqonAJRlRDZtfiR1mtnqMT/XjH0tZnaHmT0W/Ew4OlaPcYM1dx+RdKqZNU9mJQAcFy6VtE5Sp6S/lfRps5+ZBP4mSb8laaGkZkn/Q5LMbJmkb0r6a0kdeflXzWzBmOe+Rdm0vNmSnhl7UDM7W9I7JT3P3WdLulzSRne/RdL/lHSDu7e5+0X5U3ZKerWk9rw+HzOzS9x9v7J55F359m3u3iXp9yW9VtLPS1oqaY+yOeeRL0p645jHl0vqdvcH88f/T9KZeRs8KOlY/xj2YUlnSbpY0hnK/jL35/nv3qPsr3ULJC1SFrAmo2P5h/1vSvpHSfMl/S9J3zSz+e7+m3nd/jZvhzsK6nG1pA9KmidpvaS/yfc9S9Ltkr6Qv9arJX3SapibP8ZrlV1T541X1zHbh9eXsqBzjqST8+f+jqQDRx7M3V8q6XuS3pm/5iclfTx/7kpl5/838mMcdqmkDcra+W9qeE3h+Xf3+yXtlvTKMdu+RdLn8//Xcg3+vKRzlV1zAICj1+3uq8b8XDv2l+7+cne/IPj5eo3736rsveiw5XnZuGqZTrRB0vfN7M/M7A8P/9RYKQAnjmfc/V/yP/JcJ2mJsg+xh33W3Z909wOSvqQs0JCyqVk3u/vN7j7q7rdLWi3pF8c893Pu/ri7D7v7oSOOOyJpprIP9TPcfaO7P11USXf/prs/7ZnvSLpN0n8Z53X9jqQ/cfct7n5Q2QjKr1o83ewLkl5jZq354zcpC+AOH/sz7r5vzH4uMrM54xw7kQfA10j6A3fvcfd9yoLSq/NNDilr+1Pd/ZC7f69gKuOrJD3l7v+at+sXJa2V9EtHUZ0b3f0+dx9WFngcPqevVhYwfzbf90OSvirp9Uex7w/lr+9AjXUtur4OKQvSznD3EXd/wN37Jjp4Psp1taQP5Odso6SPKguiDuty94/ndUoCwCNNcP6vU3YvHA6kL1d2PUm1XYN/4e77a6kHAGBK3C/pTDM7LR8Iu1rSTRM9qZZg7WlJ38i3nT3mBwDG+slaGHcfyP/bFv1e2XTGw787VdLr8ylqvWbWK+nFygKOw8bO8f4Z7r5e0n9X9gF2p5ldb2ZLi7Y3syvN7J58el6vsqCws2j7vH43jqnbGmUBYjKVLq/LGkm/lAdsr1H+gduyaeUfzqey9UnamD9tvGNHFkhqlfTAmDrdkpdL0t8pG+W6zbJpnkWpgZfqiFHK/PGE8+fHGO+cXnrEOX2zpMVHse+x57yWuhbV5V8l3SrpejPrMrO/NbMZNRy/U9KMI4575DELr8sj1XD+/03ZdTNL0hskfc/dt+W/q+UarLkuAIBymdkvW5ag7IXKZn7cmpcvNbObJSn/w+Y7lb0nrZH0JXefcJ30hAui3f2D+cFax3wAA4CybJb0r+7+X8fZZtwkF+7+BUlfMLN2Sf9X0keUjYD8zPPMbKayEZ7fkPR1dz9kZv8h6fB0zeg4myX9trt/v5YXo59OhWxQlqBjfV7+JmVZn16u7IP6HGXT2aJ8wfuVBWSH6z02yOlWNo3vfHdPpk/kI23vkfQeM7tA0p1mdr+7f+uITbuUBQFjnaIs8KvXZknfcfdXFPz+Z16f4iBu7Lk45rrmI7EflPRBM1sh6WZl03U/PcFTu5WNyp0q6Ykxxxzb5keTfGXc8+/uW83sh8pSP79F0v8Z89zCazB/TUdbFwCYdK5SUuJPC+5+o6Qbg/IujZkp5O43K3sfqtmEI2tm9kIze0LZlBOZ2UVm9smjOQgAjOPwiMLl+ehDi2Up9JfX8mTLvhvrpXkgNqgskBnNf71D0gr7aYKNZmVTJndJGrYsCcrYdUI7JM0/YmriP0v6GzM7NT/eggkWE1+f7/N39dNpbFI2I+GgsrVJrcqmLhZ5RNL5ZnaxmbVoTPIKdx+V9C/K1totzOu0zMwuz///ajM7I58uuVfZCMzokQdQ9mZxlpm9ybJEKL+m7HtfvjFOvWr1jXzfbzGzGfnP88zs3Pz3D0t6nZm1WpY05W0T7O+Y62pmLzGz5+TTGvuUBWBRe/yMfDrvl5Sd+9n5+f9DZdfrsajl/H9e2dcHPEfS18aUH+01mLAsKU1L/rA5v89OjE9RADCN1TIN8h+UzZ3fLUnu/oikyyazUgBOHO6+WdmIwx8rC6I2S/oj1f7VIjOVJdzoVjYVbqGkD+S/+3L+724zezAfdXqXsg/he5SNdvxkvri7r1U2MrYhn3K2VNL/zre5zcz2SbpHWWKJotezTdIPJb1I0g1jfvV5ZdPotiobqblnnH08KekvJd2hLBvi3Uds8j5lUx3vyafU3SHp7Px3Z+aP+/N6fNLdvx0cY7eytWXvUda/v1fSq929u6hetcrb+ZXK5uN3KTsvH1F2riTpY5KGlAXH12mCRCt11nWxpK8oC9TWSPqOsqmRtfh9ZaOAG5Sdgy9I+kyNzz1SLef/RuVTHo+YyXJU12CBdcr+kLFM2RScA0pHKwEAFWPxuvMxG5jd6+6XmtlD7v7cvOwR/2lmNQAAUAIze1rSfxsnAycATAunrDrd33vfh6a6GjX7/cZfe8DdV011PY5Uy5d4brbs+2w8X5T9bmV/nQQAACUxs19RtvYs+a40AMCJqZZg7XeUTcFYpmz6xm2S3jGZlQIA4ERiZncpW4f3lnxdIgAANQVr7u5vnvSaAABwgnL3X5jqOgBA2Uat1uXnKFJLC95jZl/Ov5voqDJHmdkVZrbOzNaP810/AAAAAIAj1BKsnSXpWmXfS/SUmf1PMztroiflaZI/IelKZVM73mhm59VTWQAAAAA4UdTypdgu6XZJt5vZS5R9x8zvmdkjkt7v7j8seOrzJa139w2SZGbXK0vP/UTB9uqcPdNXLJiV/qIhHdAbbWwM99EweCgpG2prCbaMDTbPCMutIGvmwca0CRsKvpvUg+++HSkYHm4oWLLQGNTjYEN8Gpt9OCkbsnjbkYK4PXolRcOrFmxdtN9o24Ojcd0arfblG8MeH68p2Ed0PrLyWGPwm6JtR4J6RK9ZkhosLR/1uG6NwbaSZEH5wZG4PUeDXQwdiu+nIk1NtZ+T0dHguh+JX99IsK0ktcwcScqGhuI6R9uOFlS3IbhcovMhSc0N6X4laXAkrUfR+Qu6MhUl5G1sqP07jovukeh+KDpeVLfoupKKz9PIaHq85qa43Txoo+jazMoL7tWg/OBQ3Ac0NqY7L2rjwjYKdt3UUNBXB/uO+iEpPk8jBa+5qP89mn4kqnPRax4tOGK079bG9P1XkoZG03ukqC+LSovqMKOgPQ8F12HRvKCoXy6q28Bw3KfOCNozeg/Itk3vh32DzeG2Lc3xvXNoJN33oUO1TzWbWbDfoeF0HzNnxNsWie7JoYK6tbakn1H2DxZ8npkRn+ui/joyEOy7aL/R9VL0Hn5gf0GdT0r3XfReFF3iUdsPbu7Sod17+H7EE8yEwZqZzZf065Leouw7cX5f2fe9XKzsO4xOK3jqMmXfl3TYFk3wvTArFszS6r96ZfqL1rQj2z+vLdzHrHXbkrJnXnR2sGXsyeVLwvKGgneyTbPnJ2UtI/Eb1nDwTt/b1Bpu2zZyMCxvHR5Kyja0dIbbnjLUk5Rtau4It+1THNAOB8FWs+LOuyko7/WT4m2DN9kNA/PCbdtnxG0RddI9B+Pjdcw8kJQVBXbDwRu9JLU3pfUo2kfPUFqPog/7rU3p9TIwHP/RYO6MwbA8aouN/XPDbQeG0tt+4/bZ4bZFFnak7VkkeoPs3Rd/OOnvj7ukc1buTco2bInrfM6KdNuBg/G3ism+AAAgAElEQVR+W2emHxbaZsb37/JZfWH5+r3pdVt0vOYgyC36QF1Uj0j7zPge6R2cmZQNDcdBbmtz2hZFgUhfwYfL3v3p8U6Zvy/cdigIcoeCD6FScXtGr2X9xvi6mDs3bc+21riNoz8wSPGH5872+F6Y25Kek87mgWBLqXsofR/oOxi3cRRUS1LrjPS1DBYEF50npfUYLPjjTtE+BoO2v3jujnDbTYNzkrKifj26HwZG4v5wYfP+sLzrYHoNtDSm17ckNVt6Ttsb47o9uHtxWL64La1H78H4PXXprPR+uHPtyeG255zcG5bv7EvfX7Zsjz9LRM46Ne7LNu1IP1etXBJvWyS6JzftCP4IL+mS09OvSVz91IJw21MW94flUR9eZPXj6WelFSfH11D0ntrUGPeHj98bfwY7+YK07QYGCwYagq4vavsHX/GG8PlV5Sr+YwtqV0uCkR8q+wLR17r7ljHlq83sn+utgJldI+kaSTplfu2dDQAAAAAcz2oJ1s72gm/OdvePjPO8rZLG/rloeV525D6uVbYmTqtWdtQ+ng0AAAAAx7FagrVOM3uvpPOln86Vc/eXTvC8+yWdaWanKQvSrpb0pmOtKAAAAIDpwpgGWYJagrV/l3SDpFcr+4Lst0raNdGT3H3YzN4p6VZJjZI+4+6PH1Mto8m8Bfaevzwp62+N545H69C2tMVruqK1YpLU1ZTOxb/gYFe47dpZ6Xz3AcVz8Yeaak/40Kx4zvba5kVJWfdoPHe8aOH7+n1pewwXrOdonXEUa16C9RgbtreH2xatH2mfla7R6O5N18xI0ool6Xz3ov12742vl+6edN/Ll8RrUFqC9T/ReiVJOmVOMK+9YM1a94GCNYBBMoO+A/Gal2j9z8ql8bqE+34Urx+IFoy3nhRfh21B+XCwkF2SFnbGa0XaWtJzHa1Nk6TBIFlK/4G4PaP9Fq2b2rgvvdeleJ1W0Zq8gQNB2y+L13TdeV+8fraxL319SzfG133/nHQ9TtOh+LqPtt03Nz6njcPxPuYsSs/fY2vjdmtrC9YLBmXjmXlH2j8NXhjfk3t/lN7X2xfEa9Y6C67DzmXpmtHvfWdZuO3PXZa+D3Ttjdda9w2k1+dgQQKdzjnxutWug2nfvqIzvq+L1lNFBg7FHxO6g3VTn386Tvjc3pa2c19/fE8u7kzXAG7cEr9vRWtZJWn77nRJRVH/FK1NWjg3XodYtJ45Wu9X9H6mYNlbUZKLnqBvkaQnN6T7XnFyvKYr2veGrfG6zv1r0utzbUHCnqL1xcNB3zCjOz7Xd/Wl5fv3xtvO+F68BruvIz2v/e3x+vD2PWmdd60pWE93e3p933l1fD+19sf36raH075v/+y4bg1BAqSD96bPH+yN31twfKslWJvv7p82s3e7+3ckfcfM7q9l5+5+s6Sb66ohAAAAAJyAagnWDv9JbJuZvUpSl6R4+AkAAAAAUIpagrW/NrM5kt4j6eOS2iX9waTWCgAAAMC0xpq1+tXypdjfyP+7V9JLJrc6AAAAAABpnGDNzD4uFXxduyR3f9ek1CiyJ/3Swln98SLrBy57TlK2cE+8CHm4MV0U2jkYL/bfOCv+0sPoLwYPz0qTnEhSQ9CcO0fjBef/sTn+Iu/oC1jveyxOAnFgT7pQt2B9tGbNiRfaz3k0rV/PwnjbC+9OF3V3L4sXdS9fly6StbPjJC6v/1D8/Xu3/W6aBOBtfxcvyL7t99J9L18TX/57XxzXY8bS9HU/FrSxJM3fkb6+Q2fFX775g73p+WubfXSJFqJkKaPxmnUtXJC2W1FCjIN74zbavzVt544X7I63vSFNlNE4O65c13lxcoiRb6Rt1L47XtT96IvSdn7RN+MF9WvfnuZKOvRYfE8eOCXuc1728flJ2bo3xv3Iq/85TQyw4ZK4b3lVwfW5/fT02jjn7vg67DonXcy+fWV8bUUJRubtivc7GiyGl6SRvvRePXtDfE/2LkjrsWNZfO+d9XDcB1x4Z7rvc+6Jj9d1Znq8M1bH18XOFXESgIE56XX40s3xdTj09XTf3h5f97OCXcwZiv8aXdT2P39Teg9vPzM+3tBJQXKtc+LrYuN5cbKVhVvSa2PRYEHSpqXpvmcUJHiyren91Hh2XIetD8fnryE4fbtbC9qiJS3fPRCf031nx/1TU5A8am7Be+r3bku/AHv2yni/P14Xvz47KT3e2qfihCYrV6T9YU9P3N8Hu9Wsu+LEHjvPjZOwLPtxmrxmaGbc9q0PpH3tf/l+nPym6J5c8Vj6WubuiD/odGxJy9c/P77un//VtP/t6yxIENQZ1+3i29K+6InL4uuivTut2+L16XV4a3ftyedw/BhvZG31s1YLAAAAAMcNlzRiTIOsV2Gw5u7XPZsVAQAAAAD8VO1fYAYAAAAAeNbUkg0SAAAAAI4K2SDrVziyZmYfyf99/bNXHQAAAACAJJl7nFnKzH4k6UJJD7j7Jc9GZVat7PDVf/XK9BftJyVFe+fFWXlWn3d6UjbcEGfP2dmaZlrqb4wzEbUPx5mPnmxemB5P8fF6RtPXMTQab9vZFGeHijJKdo/EmdIGRtJsXQ0Wn++B4Tjr23CQPnLnvvh4Lc1pVqWFrfHriPQejNu+/2BBRrogm9jc1jhr2GPPpN/jXpQtsb8/HnBuCbKG9fXVPjh9aF+8bef2NJtV37w4Q1Xj4vj1zQ+yau1cFmedOuXJNENV/9y4MXafF2ewnBlkg1xakPXvyYvTa6CtL77u93bEr3vJM+m+D1zcH27rT6f3WX97nK2r6VB6DR1qi7edWXCu552V1mPnzvhaXrgwzSjZtTWtryR1zI8zI158Vpp1c/BQXLeh4fT+3bJjVny8Oem11TE7vt6aGuPr5bGn5yVlZ5wcZ8Z84uk5SVl0j0nS0oVxP3LWgj1J2dod6b0uSa0z02urd3+cFW/oUPx3zL59aV+0dGH83rBle9pPtrbG11ZLc1re1hrfvz174/ss2n7n7vg6jNq56L2hoSEuX74g7hsi67ekmQoXdsTZVUe99r/Cd++J2+KM5X1J2aNPptemJC3sTK/x7QX3b1tb3D/tfiY91/NPja/Z7dvSfRdl/x0cjPvJpUvSa27vw3E2yOEZ6fkbmBXfZycHmSM3b4z7ixknxddyY096j0R9pCTNnZ32cZu2xscr0jk/PX+DBwsytA6l93VfX9Hni7RsdkF23KaC/mLP0rRuRX3cknvT9/AdL0z7zoFrLtfI2kemzVDV8lVn+DtWf3Sqq1GzP7bXPuDuq6a6Hkca75PmLZL2SGozsz5Jpiyxi0lyd497BgAAAABA3cbLBvlHkv7IzL7u7lc9i3UCAAAAMI25TKPkMqzbhHO43P0qM1sk6Xl50b3unn6TLAAAAACgNBOGu3mCkfskvV7SGyTdZ2a/OtkVAwAAAIATWWGCkZ9sYPaIpFe4+8788QJJd7j7RWVXpjDBSGS0oN4z08HCdRevDDcdbkwXoUZlkrRzTrxEb6gxPV5nf7ygvmdWuoB0e2u6yF6SFg/sDcv7mtNkBLc1nR1ue2HjtqSsYzheFL59RlyPPqULuNsUJz7oV7pYf/lIb7htZGdjmvBFkraPxuWdDelr2TkaJ54Z9vTvEkUL2YuSu3QoLd80OjfcdnFDeg20FrTbo4eWJGVFi/3bG+OED82WLvZuVrwAvENpu/UoXtRdlLym91C6SP4FMzeF2z44vCwpK2rjU5QmjJCktaNpIp8iZzSmCTgGFC8Mb1WalGG44G9YRemHVw+kry9KzCNJK2al90NRkqG+4Th5Qmdz2nZNihetP7h7cVLW3hJfh00N6T7aZ8TXW5GOpjTxQfdwfA291NYnZV1NcT/U7fH1uWEgTRqxsjW+hoY86O+DfkGSWhvi5B5zLX19G4fjxBXRPqL7VJIe7U2v71Nmp0kyxttHk6Xnr+h19I2k19bcxjjhx4DH985SS+vXH7xfSFLvaNpfnGXxBJ0eS6+XRwfS61iSLm5N3+MkqU/p8aLXLEkLG9P+sCXoFyTpwYGlYfk5rd1JWc9InDgoUtT2faNxnXuG0n0vnRl/7th5KL13BkfiSVXRe2LR+2RR+YVtO5Kyrz19VrjtL5y2OSm7e9PycNtLlqX7laT+4fRzR+9gwbkOEp6tmBl/Romu+zaL+86NQ/HngFOa089xWw7FnyXbGuN9H+mrl16jnavXTZsEI8tWnem/u/pjU12Nmv2Z/VIlE4zUMpG04XCglttd4/MAAAAAAMeolrzjt5jZrZK+mD/+NUk3T16VAAAAAAC1JBj5IzN7naQX50XXuvuNk1stAAAAADix1fSNvu7+NUlfm+S6AAAAADhOFK33Ru1qCtaOlZltlLRP0oik4Sou2gMAAACAKprUYC33EndPUyUdjSjzY0Mcqf/nlT+XlK3ojrNONY2k2bN+cPKZR1W1/oY069B9M+JsRkMjQTayA3GulrktcXaolobhtA5BNiRJ+tSWS5Ky5qY4k1hRZqeNW9LsioODcZ3POzPNfPTw43GmNDuY7mP2gjgbUmdHnJGuuydt+6amOCtef3+QtbMzPl77rLh8eKT2vDrLO/uTsqIMj09uSTNJDR6MMwS2t8VZynp602tgdDQ+p21t6TU0MBAfb8+uOKvWnPlpG32t/7Rw23nPpPvYd3qaVU+SGgqauKMjPd7CefE+rn8mvYebmuK275ib7nfDxjjzYNH1ct5pafbB7r44E9w9a9Ksf4sXxPd6e2t8vO9uSzPStbbE9/XijjT7WVdv/Prmzkrvs+798evo2RdfFx2z031s2hFnaL3lwClJWdus9NqUpI72uA948sdpZrU7RtPsnJLU3BxkS2yNj9c8I+5HovKie3Xu7PT8DQ3HF/jy+Wl/0bW/IDtub5xds6cvPSdFfVnbSWk/0tsfn9PhkbgfWdwRZP7cm2ZhlKShQ+nr/tL++L02uiebGuPzcdeOODvj4oVp3ZoL3ht29qR1XtwZ9y29++L32kcb5idlRf1vz550H4OD8TXU2lpwXwev76uPnx5u27EzzWp48Nw4K/SK5Wl50Wtua43fi+5bl/Zxr7wwzhR8x1OnJmVF19tNq+P3l5Unp1kwi/rOh7csSMpu3p72Q5K0fEnQd+6I770DXQX3zoz0fef0c+Msr1u60n1H/VPv/vgew/HtqII1M5sn6WR3f3SS6gMAAABgmnMxDbIMtXwp9l1m1m5mHZIelPQvZva/aty/S7rNzB4ws2vqqSgAAAAAnEhqmdc1x937JL1O0ufd/VJJL69x/y9290skXSnpHWZ22ZEbmNk1ZrbazFbv6ju6L2AFAAAAgONVLcFak5ktkfQGSd84mp27+9b8352SbpT0/GCba919lbuvWtAez/sFAAAAML2MyqbNT1XVEqz9paRbJa139/vNbKWkpyZ6kpnNMrPZh/8v6ZWSHqunsgAAAABwoqjlS7G/LOnLYx5vkPQrNex7kaQbzezwcb7g7rccUy3b0hG3A3PirDwvvz/NfXL3c88Nt93UlmZw6hxKMwtJ0vaZc8LyJk+zNa1sSbPDSVKb0mmeLYqzkXV5muVMkrqH09e9dGZc55Zl6b6HPY7Ph0bjbFTL56VZypoa4qxaG3albXTBOWmGSCnOXHbKovRYknTvPWl2KUmasyhtz7ntcYaqKOPeaPwy1L0nzrbU25tm1SqyfWe6D38mzqx30tlpBq6ijJv7NsTXYZTxq7czbouZz6TZveYMxMfb/5w4K9rQULr9ud+Ns/51rUzrseTBeNuW/XE9+uan99kzrXFbDK9M69xakLlscCi97ouyPhZlGt3Qld6rF67YHW67c3d6XRRdh0WZW89YlmYT6x+Mr80Vc9P7776Ni8NtowxqW3bFmSPbCrIoPvhE2qdeeHbcH27cll4DUYZBSdqwNc6MeM7p6etb8724vxhemvYXjd+Js9XuWFLQj2xK752tZ8bZPLeNpq/PZsfttnFT2s4LF8TLAooy9y7tTPuR9Zvi95Gli9KyqM+S4uyxkvSD1WlmvVUXx9d91N8vnB+32/ZdaT95xilxBr39O+LZOAOz0/O3flvc/65ckb7vrLkvvY4lacm58Xvtps3p+WsveC+K3gdG2uJ7/UBv/BFt3e70Orz4+/G9+ujPpdfFebekGYgladfS9Jrt7IrrMFzwdjivMc2A+J9rCjLsdqU7iT+JSLNmxRl9D9yevpbd7XF7nnNPeo3PWRRvO9SS9g3zFsbbBm9PkqSGICPo1oIM4KetSevWdVraJ3vBZwMc3yYM1sxsgaT/KmnF2O3d/bfHe14e1F1UZ/0AAAAA4IRUS+r+r0v6nqQ7lH25NQAAAAAUcplGKrwWbLqoJVhrdff3TXpNAAAAAAA/Ucvk12+Y2S9Oek0AAAAAAD9Ry8jauyX9sZkNSTq8YtbdCzJgTIaGNKZsORAnAbCHNidlr9gXL2TW1njhe+hgvMhaT+xIy+bFC5l1yclp2S1r4m0fDfYrSeeni7rVEy/KV3/QRr0FbfFUvDD8kSf+PClrHo7b4tzvPp4WzosTwejJnWnZafGibv1pQV6aS5enZfduibddtSwtezyogyQtiZMZ/PCzb0/KBmbGC9zn7k8Xdbcujq/ZxTvT63C4OV5m3bw0bvs56kkLt8XJXXRakJjjP34Ub9sQXG+StG5XXB6JFqhvLqjbd58Jix9Z9/8lZZ29cdKB/tZ0ofaKTfG5HmwJkq18aXVct/PjxBz60LfTsujalKT/WJuWXX5GvO33N8Xl53YmRT/88jvCTXsG04QBn9qUJmGSpJ7+9LpfsjXuF3YviLv/+fuCc/Lv28NttTxI7vFMLW9JY3xza1r2o4LjNQZTcQ4U9OsPbYvLo/vyzIJ+69b1adnKOKHJQ9/+w6TsgrXxvTDcFPcNJz09kBYW9QEDQV80oyC1w5qCfnJPcLw3B69ZklqDbBTb4oRSWhC8Z1xUcO8V9eHBeXrou/8j3LR9f/r+uXxud7htz8H4vWHJmuC+LmrPzb1p2TMFn0V2pe8jkqSXnp6WffrueNvVQTaZB+Pr+6FH/zQpO2d9/J7a3Rn3AW396WeMqJ+VpNbT0iQ6c9YE97Qk7S34nPPj4H6fFR9PI8E1t+YoPhMV3L8aKlgh9P3082h4fUt6aPUHkrLl29M6vKKlKz5WhTnTIOtWSzbIuHcCAAAAAEyamv6MaWavkXRZ/vAudz+qL8cGAAAAABydCdesmdmHlU2FfCL/ebeZfWiyKwYAAAAAJ7JaRtZ+UdLF7j4qSWZ2naSHJKUTbAEAAABA0ihr1upW61ehj/2K+CA7AQAAAACgTObu429g9kZJH5b0bUmmbO3a+939hrIrs2plh6/+q1emv5iZZpLyk4LsUpL+8cork7L2kTjbT19jmjWuQXF7DFmc2Wm70oxId3WdEm7btSvNAtS1Nc4cOWMojqNnL0lfy9w5cZbBkVvSrHGbz40zKl10Z5xHZrg5bY/mwbhuGy5I6/bKz8b73XjRoaSsbU+83zvfEGf9O+uRtO3ad8fnqSFI1tR1elqH8ax4LM0w1bI//ovRlrPTLHMNo/F+n7w4PScrH0+vTUkanhFfn637av27S2xhQRa+tp54v+tXpddc84G4La74pzRj5qOXx9mzFm6Ij9d1Ttp4zQXJwS6+Ob0Gtp8Zb9u7JN3vwJyCNt5bcK7PTV/LhbfH/VNLkIysvyOuW9/C+IJZ/ljaRk1xF6D1L0j3MXdb/Dp6l6Sv+we/HGT8k9SxPb5e+uembbHykThj6tIn0/O04ZL4ntx4fvwCT1mb3pMD7XG7zd2RHq9o24bRuI06N6f76DozrnN7d7rtzlPj7JO9nWn50g0FGfT64ntk5ylpPaL+W4r78KgOktS2N+5To76oqI9r7Uv3MXdnvN+1z0tv7OaD8Ws+46H42or6+8G2uC2ic9qztKB/2ljQFsGpWv54vG3U5yxdG7++x14Wn5PBWelrie4nSeo6K30t/fPiE3XBXekLWf2q+PNT55a4D4iuudUvizN/vvjr6eenJ14Yd+xF10DzYHqvbjw7rvNJs9P2PPU7cVbLwVlB31lwzXZuicsvuCNto+/+RtyXRe+1c3ekZXfdfal6966eNkNVS1ad5W9d/U9TXY2afcQuf8DdV011PY407jRIMzNJd0t6gaTn5cXvc/eC3MgAAAAATnQuacSnTWxZWeMGa+7uZnazuz9H0k3PUp0AAAAA4IRXy9ypB83seRNvBgAAAAAoSy3ZIC+V9GYze0bSfmXr1tzdL5zUmgEAAACYtsgGWb9agrXLJ70WExhpSxcR97fFiTne/fX/TAvXFiyxGwwW77bGi7o1q6C8sy0tmxk364E5aYKR3iXB8yV1du8Ny0f708HQmXdviut29uKkyEfim8aesz/eR6Sh4MYL2u6p/xsnW+no3ZeUNQ/Fi6m3LE0TpUhS/yVpEo6Ovnghc0972s4DzfE5HWqKz9/i3t6krG0gXsjctj8t722fFW67vWNuUjZ8WbxgeWXXjngf89N9DM6Ik1x09qVtv3RHT7ht16I4+0XTSLpovWkkXrT+5J8sScresmtXuG2RpdvT+t13/hnhtgv3pglpOoLXLElt/el5GmiNkxZ0dseJbhpG09c948LucFsNpIvL9z/31HDTvva0v5Ck0eD+i8okqWXw6JLoJHWYHddh8Y49YfmsvWlCkn3Pja/7qA9fuDO9xySp8VCc8OHg6ek9XNQWQ83p/dAyGC/2H26K779Z+9LkB+vOXh5ue/YdjyZlIysXhNs29gaJXC4KN9XBzjhpU09HWt46cDDcdvvCtL9YsWlnuO1Qc9wfRuVNw3EfELVz0X0WvQ8UndPZM+L+0BelSSOsp+A97qKgzgfj9yJdFr9fK+hHHvn7+O/Z56zfkpRtWJG+V0vS8q64H4navr0vTgY02JLeI7N3x/3hrl+en5TN7Y3fU7uuSreV4vei5z/0ZLjt0JvSezKqryTN2RV/JlJ/eo1vPTn+3BH2979UdB2mfWdRGzeMxslrGn9+d7BxweeneUE/GdR31RMb4ufjuFZLsDZ+ukgAAAAAQOlqCda+qSxgM0ktkk6TtE7S+ZNYLwAAAAA4oU0YrOWZIH/CzC6R9HuTViMAAAAA05rL5KxZq9tRf5Ouuz+oLOkIAAAAAGCSTDiyZmZ/OOZhg6RLJHVNWo0AAAAAADWtWRubYmpY2Rq2r05GZR748Z5u+/UbnpHUKakgnRqmAc7f9Mb5m744d9Mb52/6OuLcfWXKKoJjMl3uvTh9cIWNHv0kPhyhljVrH5QkM2t19zhvaUncfUF+rNXuvmoyj4XJw/mb3jh/0xfnbnrj/E1fnLvpjfOHKpsw3DWzF5rZE5LW5o8vMrNPTnrNAAAAAOAEVss0yH9Q9sXYN0mSuz9iZpdNaq0AAAAATGujTjbIetU0kdTdNx9RNDIJdRnr2knePyYX52964/xNX5y76Y3zN31x7qY3zh8qq5aRtc1m9iJJbmYzJL1b0prJrJS7c9NMY5y/6Y3zN31x7qY3zt/0xbmb3jh/qLJaRtZ+R9I7JC2TtFXSxfljAAAAAMAkmTBYc/dud3+zuy9y94Xu/uvuvnuyKmRmV5jZOjNbb2bvn6zjoH5mdrKZfdvMnjCzx83s3Xl5h5ndbmZP5f/Om+q6opiZNZrZQ2b2jfzxaWZ2b34P3mBmzVNdR8TMbK6ZfcXM1prZmjwhFPffNGBmf5D3m4+Z2RfNrIV7r7rM7DNmttPMHhtTFt5rlvnH/Dw+amaXTF3NIRWev7/L+85HzexGM5s75ncfyM/fOjO7fGpqPf25pBHZtPmpqsJgzcz+fJyfP5uMyphZo6RPSLpS0nmS3mhm503GsVCKYUnvcffzJL1A0jvy8/V+Sd9y9zMlfSt/jOo6cmrzRyR9zN3PkLRH0tumpFaoxf+WdIu7nyPpImXnkfuv4sxsmaR3SVrl7hdIapR0tbj3quxzkq44oqzoXrtS0pn5zzWS/s+zVEcU+5zS83e7pAvc/UJJT0r6gCTln2OulnR+/pxP5p9PgSkx3sja/uBHyt483jdJ9Xm+pPXuvsHdhyRdL+mqSToW6uTu29z9wfz/+5R9UFym7Jxdl292naTXTk0NMREzWy7pVZI+lT82SS/VT7/RlfNXUWY2R9Jlkj4tSe4+5O694v6bLpoknWRmTZJaJW0T915luft3JfUcUVx0r10l6fOeuUfSXDNb8uzUFJHo/Ln7be4+nD+8R9Ly/P9XSbre3Q+6+48lrVf2+RSYEoUJRtz9o4f/b2azlf31/beUBVAfLXpenZZJGpt5coukSyfpWCiRma2Q9FxJ90pa5O7b8l9tl7RoiqqFif2DpPdKmp0/ni+pd8wb2BZl9yWq5zRJuyR91swukvSAsn6a+6/i3H2rmf29pE2SDki6Tdn5496bXoruteizzDJlATmq6bcl3ZD/f5my4O0w7sVjZnJS99dt3DVr+Xzsv5b0qLLA7hJ3f5+773xWaodpwczaJH1V0n93976xv3N3VzZtGRVjZq+WtNPdH5jquuCYNOn/b+/+Y6xNz7qAf695jSSWGtAFmqU1EmmQBmk1m6Zaoq3d1LWpbZQiYCOtSEgTC5qYoHVVGohJkxqNCUa6av/bSBpgs5t06e5WKaR/tN2tLP0twlpktyqsRdpm4493zuUfc7aM6zvzvjvn3j3X6Xw+yWTO85znPHNvJued/c51XfckfyLJv+juP56T7of/p+XR+2+m7WzT63MSuG9O8pz8/y1aHBDvtcNVVbfnZKzjzn2vBa7lvJm1dyZ5MMkXk/yx7n57d//2M7yex5K84NTx87fnGGr75xx+Jsmd3f2z29P/7cmWj+1n4X6mlyd5XVV9NicV8z+bkxmor9m2ZiXeg5M9muTR7v7w9vincxLevP/muzXJf+ru3+ru/5PkZ3PyfvTeOyxnvdf8v68Rj78AABLySURBVMyBqKo3J3ltkjduA3fi+8cw51XW/nZOfuP395N8rqq+sP34YlV94ZzX7eLBJC/c7oj1e3My4HnPM/S12NF2vulfJ/l0d/+TU0/dk+RN28dvSnL3s702rq+739bdz+/uP5yT99q/6+43Jvn5JG/YXub7N1R3/9ec/B3Mb9meelWST8X77xD85yQvq6rft/139MnvnffeYTnrvXZPku/b7gr5siS/c6pdkiGq6racjAG8rrufOPXUPUm+p6q+qqq+KScbxXxkH2uE5PyZtRv5G2xLdffVqnprkvtysjvWu7v7k8/2OrhhL0/yV5N8vKoe3p77e0nekeQ9VfXXk/x6kr+8p/VxMX8nyU9tW6B/KdsNLBjph5Lcuf3l1iM5mSs+ivffaN394ar66ST/PiftV7+U5I4k74333khV9W+SvCLJTVX1aJIfzdk/6+5N8pqcbEzxRE7el+zRGd+/tyX5qiQPnPzOJB/q7rd09yer6j05+QXK1SR/o7uP97Pyw9ZJNoO3xD8U9btVXwAAgN193S1/tL/zw/9y38u4Ye/6PX/6o919y77X8VTPevUMAACA6zuzDRIAAOBCOjm2df/OVNYAAAAGEtYAAAAG0gYJAAAsZTfINYQ1gEugqv5gkn+7PXxekuMkv7U9fqK7/9ReFgYAnElYA7gEuvu/J3lJklTV25N8qbv/8V4XBQCcy8wawCVXVV/afn5FVf1CVd1dVY9U1Tuq6o1V9ZGq+nhV/ZHtdV9XVT9TVQ9uP16+3/8CAPjKpLIGwGkvTvKtST6f5JEk/6q7X1pVfzPJDyX5W0n+WZJ/2t0frKo/lOS+7WsA4Mva1v07E9YAOO3B7v4vSVJVv5bk/u35jyd55fbxrUleVPXlH8K/v6q+uru/9KyuFAC+wglrAJz2v0493pw63uR3f2YcJXlZd//PZ3NhAHDZmFkD4Om6PyctkUmSqnrJHtcCwEiVzQF9TCWsAfB0/XCSW6rqY1X1qSRv2feCAGBfquq7quqTVbWpqlvOue6z2w27Hq6qh27k3togAS6Z7n77U46/evv5A0k+cOr8K049/vJz3f14ku9+hpcJAIfiE0n+UpJ33cC1r9z+HL0hwhoAAMAFdfenk+TUxlvLCGsAAMBSnWRzWFv33/SU1sQ7uvuOxV+jk9xfVZ3kXTdyf2ENAAC47B7v7vPmzd6f5HnXeOr27r77Br/Gd3T3Y1X19UkeqKrPdPcvnvcCYQ0AAOAc3X3rgns8tv38m1V1V5KXJhHWAACAZ9fxYbVBPqOq6jlJjrr7i9vHr07yY9d7na37AQAALqiq/mJVPZrkTyZ5b1Xdtz1/c1Xdu73sG5J8sKp+OclHkry3u993vXurrAEAAFxQd9+V5K5rnP9cktdsHz+S5MVP997CGgAAsFxHG+SutEECAAAMJKwBAAAMJKwBAAAMZGYNAABYqpNsbN2/M5U1AACAgYQ1AACAgbRBAgAAa3XlWBvkzlTWAAAABhLWAAAABhLWAAAABjKzBgAALHWydf++V3H4VNYAAAAGEtYAAAAG0gYJAAAs17bu35nKGgAAwEDCGgAAwEDaIAEAgKVOdoPUBrkrlTUAAICBhDUAAICBhDUAAICBzKwBAADLbWJmbVcqawAAAAMJawAAAANpgwQAAJbqJMe27t+ZyhoAAMBAwhoAAMBAwhoAAMBAZtYAAIC1utJm1namsgYAADCQsAYAADCQNkgAAGC5zUYb5K5U1gAAAAYS1gAAAAbSBgkAACzVSY7tBrkzlTUAAICBhDUAAICBhDUAAICBzKwBAABrdbIxs7YzlTUAAICBhDUAAICBtEECAADLtTbInamsAQAADCSsAQAADCSsAQAADGRmDQAAWKpTtu5fQGUNAABgIGENAABgIG2QAADAcpve9woOn8oaAADAQMIaAADAQNogAQCApbqT443dIHelsgYAADCQsAYAADCQsAYAADCQmTUAAGC5bjNru1JZAwAAGEhYAwAAGEgbJAAAsNxGG+TOVNYAAAAGEtYAAAAGEtYAAAAGMrMGAAAs1UmON2bWdqWyBgAAMJCwBgAAMJA2SAAAYK0uW/cvoLIGAAAwkLAGAAAwkDZIAABgqU7Sm32v4vCprAEAAAwkrAEAAAwkrAEAAAxkZg0AAFjO1v27U1kDAAAYSFgDAAAYSBskAACwViebjTbIXamsAQAADCSsAQAADCSsAQAADGRmDQAAWKqTHNu6f2cqawAAAAMJawAAAANpgwQAAJZrW/fvTGUNAABgIGENAABgIG2QAADAUp1k0/texeFTWQMAABhIWAMAABhIWAMAABjIzBoAALBWV45t3b8zlTUAAIALqqp3VtVnqupjVXVXVX3NGdfdVlX/oap+tar+7o3cW1gDAAC4uAeSfFt3f3uSX0nytqdeUFVXkvzzJH8+yYuSfG9Vveh6N9YGCQAALNVJNpekDbK77z91+KEkb7jGZS9N8qvd/UiSVNVPJXl9kk+dd2+VNQAAgDW+P8nPXeP8Nyb5jVPHj27PnUtlDQAAuOxuqqqHTh3f0d13PHlQVe9P8rxrvO727r57e83tSa4muXPVooQ1AADgsnu8u28568nuvvW8F1fVm5O8NsmruruvccljSV5w6vj523PnEtYAAIDlui/HzFpV3ZbkR5L8me5+4ozLHkzywqr6ppyEtO9J8leud28zawAAABf3E0mem+SBqnq4qn4ySarq5qq6N0m6+2qStya5L8mnk7ynuz95vRurrAEAAFxQd3/zGec/l+Q1p47vTXLv07m3sAYAAKzVyWaz70UcPm2QAAAAAwlrAAAAA2mDBAAAluokm83l2A3ymaSyBgAAMJCwBgAAMJCwBgAAMJCZNQAAYK1Ojs2s7UxlDQAAYCBhDQAAYCBtkAAAwFKdsnX/AiprAAAAAwlrAAAAAwlrAAAAA5lZAwAAluvNvldw+FTWAAAABhLWAAAABtIGCQAArNXJcdu6f1cqawAAAAMJawAAAANpgwQAAJbqJJuNNshdqawBAAAMJKwBAAAMJKwBAAAMZGYNAABYbrPZ9woOn8oaAADAQMIaAADAQNogAQCAtTppW/fvTGUNAABgIGENAABgIGENAABgIDNrAADAUp1kY2ZtZyprAAAAAwlrAAAAA2mDBAAA1urkeLPvRRw+lTUAAICBhDUAAICBtEECAABLdcpukAuorAEAAAwkrAEAAAwkrAEAAAxkZg0AAFirkz42s7YrlTUAAICBhDUAAICBtEECAABLdZLjzb5XcfhU1gAAAAYS1gAAAAYS1gAAAAYyswYAACy32di6f1cqawAAAAMJawAAAANpgwQAANbqZGPr/p2prAEAAAwkrAEAAAykDRIAAFiuDmg3yN73As6gsgYAADCQsAYAADCQsAYAADCQmTUAAGCtTq4cH87M2tV9L+AMKmsAAAADCWsAAAADaYMEAACWqiRHm32v4vCprAEAAAwkrAEAAAwkrAEAAAxkZg0AAFirK0ebw9m6fyqVNQAAgIGENQAAgIG0QQIAAMvV8b5XcPhU1gAAAAYS1gAAAAbSBgkAACxVnVyxG+TOVNYAAAAGEtYAAAAGEtYAAAAGMrMGAAAsd7TZ9woOn8oaAADAQMIaAADAQNogAQCApaqTo2Nb9+9KZQ0AAGAgYQ0AAGAgYQ0AAGAgM2sAAMBytTGztiuVNQAAgIGENQAAgIG0QQIAAEtVJ1eO972Kw6eyBgAAMJCwBgAAMJA2SAAAYLHKkd0gd6ayBgAAMJDKGgAAwAVV1TuT/IUk/zvJryX5a939P65x3WeTfDHJcZKr3X3L9e6tsgYAAHBxDyT5tu7+9iS/kuRt51z7yu5+yY0EtURlDQAAWK2To0uydX9333/q8ENJ3rDq3iprAAAAa3x/kp8747lOcn9VfbSqfvBGbqayBgAAXHY3VdVDp47v6O47njyoqvcned41Xnd7d9+9veb2JFeT3HnG1/iO7n6sqr4+yQNV9Znu/sXzFiWsAQAAS1WSOqyt+x8/b46su28978VV9eYkr03yqu7uM+7x2Pbzb1bVXUlemuTcsKYNEgAA4IKq6rYkP5Lkdd39xBnXPKeqnvvk4ySvTvKJ691bWAMAALi4n0jy3Jy0Nj5cVT+ZJFV1c1Xdu73mG5J8sKp+OclHkry3u993vRtrgwQAALig7v7mM85/Lslrto8fSfLip3tvYQ0AAFirkyuXZOv+Z5I2SAAAgIGENQAAgIG0QQIAAEtVkqPNvldx+FTWAAAABhLWAAAABtIGCQAArNXJ0XHtexUHT2UNAABgIGENAABgIGENAABgIDNrAADAcmXr/p2prAEAAAwkrAEAAAykDRIAAFiqOrli6/6dqawBAAAMJKwBAAAMJKwBAAAMZGYNAABY7uh43ys4fCprAAAAAwlrAAAAA2mDBAAAlqpOjja27t+VyhoAAMBAwhoAAMBA2iABAIDlym6QO1NZAwAAGEhYAwAAGEhYAwAAGMjMGgAAsFZXrhzbun9XKmsAAAADCWsAAAADaYMEAACWqk6ObN2/M5U1AACAgYQ1AACAgYQ1AACAgcysAQAAyx1t9r2Cw6eyBgAAMJCwBgAAMJA2SAAAYK1O6rj2vYqDp7IGAAAwkLAGAAAwkDZIAABgqUpy5Xjfqzh8KmsAAAADCWsAAAADCWsAAAADmVkDAADW6uTIzNrOVNYAAAAGEtYAAAAG0gYJAAAsVUmOjmvfyzh4KmsAAAADCWsAAAADaYMEAADW6qQ2+17E4VNZAwAAGEhYAwAAGEhYAwAAGMjMGgAAsFQluXK871UcPpU1AACAgYQ1AACAgbRBAgAAa3VydFz7XsXBU1kDAAAYSFgDAAAYSFgDAAAYyMwaAACwVidHtu7fmcoaAADAQMIaAADAQNogAQCApSraIFdQWQMAABhIWAMAABhIGyQAALBWJ0fHte9VHDyVNQAAgIGENQAAgIGENQAAgIHMrAEAAEvZun8NlTUAAICBhDUAAICBtEECAABrtTbIFVTWAAAABhLWAAAABhLWAAAABjKzBgAArGVmbQmVNQAAgIGENQAAgIG0QQIAAEtVKkfHte9lHDyVNQAAgIGENQAAgIG0QQIAAGvZDXIJlTUAAICBhDUAAICBhDUAAICBzKwBAABLlZm1JVTWAAAABhLWAAAALqiqfryqPlZVD1fV/VV18xnXvamq/uP24003cm9tkAAAwHKXqA3ynd39D5Kkqn44yT9M8pbTF1TVH0jyo0luSdJJPlpV93T3b593Y5U1AACAC+ruL5w6fE5OwthT/bkkD3T357cB7YEkt13v3iprAAAAO6iqf5Tk+5L8TpJXXuOSb0zyG6eOH92eO5fKGgAAcNndVFUPnfr4wdNPVtX7q+oT1/h4fZJ09+3d/YIkdyZ566pFqawBAABLnWzdX/textPxeHffctaT3X3rDd7nziT35mQ+7bTHkrzi1PHzk3zgejdTWQMAALigqnrhqcPXJ/nMNS67L8mrq+prq+prk7x6e+5cKmsAAAAX946q+pYkmyS/nu1OkFV1S5K3dPcPdPfnq+rHkzy4fc2Pdffnr3djYQ0AAFjusmzd393fecb5h5L8wKnjdyd599O5tzZIAACAgYQ1AACAgbRBAgAAa/XlaYN8JqmsAQAADCSsAQAADCSsAQAADGRmDQAAWKrMrC2hsgYAADCQsAYAADCQNkgAAGA5bZC7U1kDAAAYSFgDAAAYSFgDAAAYyMwaAACw1MnW/bXvZRw8lTUAAICBhDUAAICBtEECAADL2bp/dyprAAAAAwlrAAAAA2mDBAAA1mptkCuorAEAAAwkrAEAAAwkrAEAAAxkZg0AAFiqzKwtobIGAAAwkLAGAAAwkDZIAABgOW2Qu1NZAwAAGEhYAwAAGEhYAwAAGMjMGgAAsJSt+9dQWQMAABhIWAMAABhIGyQAALBWJ0dX972Iw6eyBgAAMJCwBgAAMJA2SAAAYLmj49r3Eg6eyhoAAMBAwhoAAMBAwhoAAMBAZtYAAIClqpOj432v4vCprAEAAAwkrAEAAAykDRIAAFhOG+TuVNYAAAAGEtYAAAAGEtYAAAAGMrMGAAAsZev+NVTWAAAABhLWAAAABtIGCQAArKUNcgmVNQAAgIGENQAAgIG0QQIAAMtpg9ydyhoAAMBAwhoAAMBAwhoAAMBAZtYAAIClytb9S6isAQAADCSsAQAADKQNEgAAWKuTo6v7XsThU1kDAAAYSFgDAAAYSFgDAAAYyMwaAACwVMXW/SuorAEAAAwkrAEAAAykDRIAAFirtUGuoLIGAAAwkLAGAAAwkDZIAABgOW2Qu1NZAwAAGEhYAwAAGKi6e99rAAAAvoJU1fuS3LTvdTwNj3f3bftexFMJawAAAANpgwQAABhIWAMAABhIWAMAABhIWAMAABhIWAMAABhIWAMAABhIWAMAABhIWAMAABhIWAMAABjo/wKQCq/u4HJDIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A hidden neuron that looks excited:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The output neuron of the label:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data, target = test_set_loader.__iter__().__next__()\n",
    "\n",
    "# taking 1st testing example:\n",
    "x = torch.stack([data[0]]) \n",
    "y = target.data.numpy()[0]\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.imshow(x.data.cpu().numpy()[0,0])\n",
    "plt.title(\"Input image x of label y={}:\".format(y))\n",
    "plt.show()\n",
    "\n",
    "# plotting neuron's activations:\n",
    "spiking_model.visualize_all_neurons(x)\n",
    "print(\"A hidden neuron that looks excited:\")\n",
    "spiking_model.visualize_neuron(x, layer_idx=0, neuron_idx=0)\n",
    "print(\"The output neuron of the label:\")\n",
    "spiking_model.visualize_neuron(x, layer_idx=1, neuron_idx=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "Well, I've trained just a bit here. My goal is not to break the benchmarks, I just wanted to make a comparison and to see if it could train. So it ends up that the results are about the same, however the SNN seems to perform a bit better, although it takes much more time to train. \n",
    "\n",
    "Full disclaimer: no to almost no hyperparameter tuning has been performed yet as I've coded this mostly one-shot and posted it, so the performances may vary a lot with tinkering. It would be worth trying a few more things to see how it goes (don't hesitate to fork the [repo](https://github.com/guillaume-chevalier/Spiking-Neural-Network-SNN-with-PyTorch-where-Backpropagation-engenders-STDP)). \n",
    "\n",
    "Using SNNs should act as a regularizer, just like dropout, as I wouldn't expect the neurons to fire all at the same time. Although, I want to point out that it's an interesting path to explore, as [Brain Rhythms](https://www.youtube.com/watch?v=OCpYdSN_kts) seems to play an important role in the brain, whereas in Deep Learning no such things happens. \n",
    "\n",
    "I think that I've discovered that backpropagation can entail Hebbian learning given a time axis and a refractory firing behavior on that time axis within Deep Neural Networks (DNN). Although I have searched online and I **haven't found** anyone explaining this link concretely, so this is why I want to explain it as follow, as it might be the first time that this is written down in words. If you found prior art, [please open an issue](https://github.com/guillaume-chevalier/Spiking-Neural-Network-SNN-with-PyTorch-where-Backpropagation-engenders-STDP/issues). \n",
    "\n",
    "## An interesting idea: Hebbian learning naturally takes place in the backpropagation of SNNs\n",
    "\n",
    "Before even explaining what I discovered and how all of this relates, a little bit of theory is needed here. \n",
    "\n",
    "### Introduction to Hebbian theory and Spike-Timing-Dependent Plasticity (STDP)\n",
    "\n",
    "First, what is Hebbian Theory? In simple words, neurons that fire together wire together. More precisely, let's see how [it is explained on Wikipedia](https://en.wikipedia.org/wiki/Hebbian_theory) here: \n",
    "\n",
    "> #### Hebbian theory\n",
    "> \n",
    "> Hebbian theory is a neuroscientific theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell. It is an attempt to explain synaptic plasticity, the adaptation of brain neurons during the learning process. It was introduced by Donald Hebb in his 1949 book The Organization of Behavior.[1] The theory is also called Hebb's rule, Hebb's postulate, and cell assembly theory. Hebb states it as follows:\n",
    "> \n",
    "> > Let us assume that the persistence or repetition of a reverberatory activity (or \"trace\") tends to induce lasting cellular changes that add to its stability.[…] When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased.[1]\n",
    ">\n",
    "> The theory is often summarized as \"Cells that fire together wire together.\"[2] This summary, however, should not be taken too literally. Hebb emphasized that cell A needs to \"take part in firing\" cell B, and such causality can occur only if cell A fires just before, not at the same time as, cell B. This important aspect of causation in Hebb's work foreshadowed what is now known about [spike-timing-dependent plasticity](https://en.wikipedia.org/wiki/Spike-timing-dependent_plasticity), which requires temporal precedence.[3] [...]\n",
    "\n",
    "Let's pause here. What is Spike-Timing-Dependent Plasticity (STDP)? From [Wikipedia, here is how it is explained](https://en.wikipedia.org/wiki/Spike-timing-dependent_plasticity):\n",
    "\n",
    "> #### Spike-timing-dependent plasticity\n",
    "> \n",
    "> Spike-timing-dependent plasticity (STDP) is a biological process that adjusts the strength of connections between neurons in the brain. The process adjusts the connection strengths based on the relative timing of a particular neuron's output and input [action potentials](https://en.wikipedia.org/wiki/Action_potential) (or spikes). The STDP process partially explains the activity-dependent development of nervous systems, especially with regard to [long-term potentiation](https://en.wikipedia.org/wiki/Long-term_potentiation) and [long-term depression](https://en.wikipedia.org/wiki/Long-term_depression).\n",
    ">\n",
    "> [...]\n",
    ">\n",
    "> ##### History\n",
    "> \n",
    "> In 1973, M. M. Taylor[1] suggested that if synapses were strengthened for which a presynaptic spike occurred just before a postsynaptic spike more often than the reverse (Hebbian learning), while with the opposite timing or in the absence of a closely timed presynaptic spike, synapses were weakened (anti-Hebbian learning), the result would be an informationally efficient recoding of input patterns. This proposal apparently passed unnoticed in the neuroscientific community, and subsequent experimentation was conceived independently of these early suggestions.\n",
    "> [...]\n",
    "> In studies on neuromuscular synapses carried out by Y. Dan and Mu-ming Poo in 1992,[3] and on the hippocampus by D. Debanne, B. Gähwiler, and S. Thompson in 1994,[4] showed that asynchronous pairing of postsynaptic and synaptic activity induced long-term synaptic depression.\n",
    "> [...]\n",
    "> Several reasons for timing-dependent plasticity have been suggested. For example, STDP might provide a substrate for Hebbian learning during development,[7][8] or, as suggested by Taylor[1] in 1973, the associated Hebbian and anti-Hebbian learning rules might create informationally efficient coding in bundles of related neurons.\n",
    "> \n",
    "> ##### Mechanisms\n",
    "> \n",
    "> [...] Due to their high permeability for calcium, they generate a local chemical signal that is largest when the [back-propagating action potential](https://en.wikipedia.org/wiki/Neural_backpropagation) in the dendrite arrives shortly after the synapse was active (pre-post spiking). Large postsynaptic calcium transients are known to trigger synaptic potentiation (Long-term potentiation). The mechanism for spike-timing-dependent depression is less well understood, but often involves either postsynaptic voltage-dependent calcium entry/mGluR activation [...].\n",
    "> \n",
    "> ##### From Hebbian rule to STDP\n",
    "> \n",
    "> According to the Hebbian rule, synapses increase their efficiency if the synapse persistently takes part in firing the postsynaptic target neuron. An often-used simplification is those who fire together, wire together, but if two neurons fire exactly at the same time, then one cannot have caused, or taken part in firing the other. Instead, to take part in firing the postsynaptic neuron, the presynaptic neuron needs to fire just before the postsynaptic neuron. Experiments that stimulated two connected neurons with varying interstimulus asynchrony confirmed the importance of temporal precedence implicit in Hebb's principle: the presynaptic neuron has to fire just before the postsynaptic neuron for the synapse to be potentiated.[10] In addition, it has become evident that the presynaptic neural firing needs to consistently predict the postsynaptic firing for synaptic plasticity to occur robustly,[11] mirroring at a synaptic level what is known about the importance of contingency in classical conditioning, where zero contingency procedures prevent the association between two stimuli.\n",
    "\n",
    "You might now see where I'm going with all those definitions."
   ]
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    "## Backpropagation engenders Hebbian learning\n",
    "\n",
    "Okay, let's proceed with the explanations. One element is missing from the above title. I should rather say, \"Backpropagation engenders Hebbian learning when dealing with SNNs\". SNNs are here the mathematical tool that enables doing the proof and making the link. Why? Because the concept of timing in STDP is important for going from backpropagation to Hebbian learning. \n",
    "\n",
    "Think about it: from the point of view of a neuron that spikes, upon performing backpropagation, the neuron will backpropagate to its inputs for which the input signal was received more recently than not, hence the signal that really contributed to firing. Why? Because older signals are decayed exponentially and their gradients vanish towards zero. So with SNNs, the gradients are mostly transferred to input that was fired just before self-firing to an output from the point of view of a neuron. \n",
    "\n",
    "However, there is still a mystery. \n",
    "- First, the gradient could still be negative despite the neurons did fire together. This doesn't mean that the behavior or the neuron that provoked the firing of another neuron will be necessarily reinforced positively. If you have any additional ideas on this, please open an [issue](https://github.com/guillaume-chevalier/Spiking-Neural-Network-SNN-with-PyTorch-where-Backpropagation-engenders-STDP/issues). \n",
    "- Second, if the input neuron fires just after instead of just before, its gradient remains unnafected or will have strongly decayed later on. To fix this second point, I at least have two ideas on how a neuron that fired too late could be penalized: \n",
    "  - Use weight decay (L2) regularization, or\n",
    "  - Have neurons output a small negative signal shortly after the positive signal, and let this negative signal be longer than the positive signal (such that the integral of the whole spike and its negative following counterpart would be null or close to null) and let this negative signal be provoked by the late neurons's signal such that their late signal be the one penalized during backpropagation.\n",
    "\n",
    "All of that said, this is a very interesting link between the saying of \"cells that fire together wire together\" (Hebbian learning) and backpropagation. Think about it. \n",
    "\n",
    "### How is backpropagation implemented in the brain? Could it be Contrastive Divergence (CD) between the brain rhythms' cycles?\n",
    "\n",
    "The question is as asked by Yoshua Bengio: https://youtu.be/FhRW77rZUS8?t=19m47s\n",
    "\n",
    "First, let me point to you at the 35th minute of [this talk](https://youtu.be/VIRCybGgHts?t=35m17s) of Geoffrey Hinton. STDP is discussed, and earlier in the talk, Hinton did talk about **autoencoders** doing **cycles**. To me, those cycles weirdly resembles [Brain Rhythms](https://www.youtube.com/watch?v=OCpYdSN_kts). This leads me to my next idea: **what if** brain rhythms were those cyclic autoencoder passes (as discussed by Hinton), between which the past and new information was compared to generate purely local gradients? The link between the two concepts seems small, but I feel like it could somehow be very very related and an important topic.\n",
    "\n",
    "> Cool idea: what if in the brain, gradients comes from Contrastive Divergence (CD) between each brain rhythm iteration?\n",
    "\n",
    "That seems like an intersting conjecture to me. It would be interesting to try. So the differences of activation values between brain rhythms (as CD is done in autoencoders) could act as a learning signal to help auto-encode information in an unsupervised manner. First, we humans are born as unsupervised machines (or perhaps more semi-supervised machines learning to minimize pain). \n",
    "\n",
    "CD is the algorithm that is used in autoencoders such as the Restricted Boltzmann Machines (RBM), such that passing the information in the neurons as a cycle enables differentiating signals to structure information into clusters and categories. Have you ever classified someone new in one of your stereotype \"boxes\"? That might be the result of clusters of unsupervised learning.\n",
    "\n",
    "Imagine for a second a deep SNN for which the top-most layer would feed back into the lower-most layer, and that CD is used to assist learning into discovering patterns just like what's done in Restricted Boltzmann Machines (RBM). This way you might already find powerful gradients provided by the CD which may help the neural network lear by himself just seeing data - even before having a precise supervised learning goal (gradient) being provided.\n",
    "\n",
    "There could also be cycles of cycles. So you could have high-frequency SNN CD cycles, and low-frequency ones using more layers between the cycles. This way each small layer can tune himself, but also the big picture also syncs up, having the top-most layers re-feeding back into other parts of the network. "
   ]
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    "### Other tracks that were pointed out by Yoshua Bengio to explore \n",
    "\n",
    "I have talked to Yoshua Bengio on this topic of the discovery I've made on how that backprop could entail Hebbian learning in the brain. Among other thing, he pointed me to a few learning resources on this precise topic (and also to read a paper a day), so I asked for what to read on the topic. \n",
    "- First, he named Blake Richard. \n",
    "- Then, he named Walter Senn.\n",
    "- He also named Konrad Kording. \n",
    "\n",
    "Watching [this video of Blake Richard](https://youtu.be/C_2Q7uKtgNs?t=11m3s), at about 11 minutes, he says that he have been thinking for a long time on how the brain could do backpropagation or something similar to it. And he says that in Deep Learning, we have stripped the complexity out of the neurons and that real neurons are way more complicated than neurons used in Deep Learning. He even says that perhaps by stripping away the complexity, we have made things harder to understand. Later when he describes his work, he talks of approximating the weight updates that would be prescribed by backpropagation in his algorithm. From what he says about ensembles later on in the talk, it looks like a good idea, especially when dealing with SNNs. Those ensembles are like having many neurons playing the role of one neuron (somehow like trees v.s. forests). I thought about that simply to provide more spikes per \"neuron\", where a \"neuron\" would now instead be a neuron ensemble with similar weights, but didn't see it as a way to share the gradient of the whole group of neurons. It could be interesting to attribute a backpropagated gradient to such an ensemble of neurons instead of just one, because SNNs' spikes are rarer than continuous sigmoid-activated output. This could avoid dead neurons (think like dead ReLUs but in SNNs because of lack of spiking). It would somehow be possible to \"cheat\" the gradients by normalizing the sum of all inner states to the sum of the amplitude of the output activations to attribute some of the gradients back to all neurons of the ensemble if one of the neurons of the ensemble fires. Interesting fact: this also makes me think of capsules networks with their voting groups of neurons.  \n",
    "\n",
    "Despite the work I present here successfully trained on the first shot after coding and debugging dimensions, I have been tempted to code an ensemble myself. What surprises me is that I've coded the present SNN before watching Blake's video, but in a different way (which I have rollbacked due to too much added complexity to this already-complex code which would make it too unreadable to you open-source code reader - oops sorry I didn't even commit it). So first, I tried to use output neurons' activation instead of their inner state as logits. \n",
    "\n",
    "One thing that struck me is that it would never learn what to do for a label if the label was never firing (like a dead neuron): the gradients would never reach the teaching signal if the neural net was badly initialized from random. This lead me to create an ensemble of neurons for each label, which now as of writing makes me thing of the [Mixture of Softmaxes (MoS)](https://smerity.com/articles/2017/mixture_of_softmaxes.html) in which many softmaxe layers are used a the output, and which is similar to having an ensemble of neurons at the output. With such modifications, the network could learn. Also, I tried to add a 3rd layer such as to have one more hidden layer as currently. This at first didn't work and required to adjust carefully the firing thresholds for every neuron not to be already dead at the start (especially in the top layers, no information was received - just [this interesting SNN animation](https://www.youtube.com/embed/3JQ3hYko51Y?start=120) - SNNs probably needs some tuning to ensure neurons doesn't end up dead easily). \n",
    "\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "I am not the first one to program an SNN for sure, although those kind of neural networks are rarily coded. However, I expose here some important behavior that emerges from using SNNs where each neuron has its excitation evolving through time and then spiking, linking to interesting resources, and providing dynamic PyTorch code as an example on how to work with SNNs. \n",
    "\n",
    "I lay out the foundations for thinking that STDP and Hebbian learning are closely related to backpropagation in SNNs. Backpropagation in SNNs could engender the STDP rule like in Hebbian learning, as in SNNs the inner pre-activation value fades until it reaches a threshold and fire, which makes old pre-activation values fade with a vanishing gradient to enforce STDP. \n",
    "\n",
    "It is also interesting that brain rythms could happen in SNNs, and that with Contrastive Divergence (CD), gradients could be provided between each cycle of the rhythm to enhance formation of clusters in the representation of information in the neurons, unsupervised learners that we humans are. I also point out to an idea of having a negative spike right after each positive spike, where negative spikes are inversely correlated to pre-synaptic neurones that fired too late for the impulse to be released so as to improve timing or respect more the rule of STDP that neurons late to the party should be penalized and would instead need to fire before their post-synaptic target."
   ]
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   "source": [
    "## Citation\n",
    "\n",
    "I'm somehow not sure of the importance of my discovery of the link between STDP, Hebbian learning and backpropagation, as it might have already been discovered before, although I didn't find prior art. I did this on my own as a side project purely out of curiosity. I'd like to see dicussion in the [issues](https://github.com/guillaume-chevalier/Spiking-Neural-Network-SNN-with-PyTorch-where-Backpropagation-engenders-STDP/issues) section on GitHub. I should perhaps write a paper if this article gets traction. For the time being, to cite my code, you can point to the URL of the GitHub repository, for example:\n",
    "\n",
    "> Guillaume Chevalier, Spiking Neural Networks (SNNs) with PyTorch, 2018,\n",
    "> https://github.com/guillaume-chevalier/Spiking-Neural-Networks-SNNs-with-PyTorch\n",
    "\n",
    "## License\n",
    "\n",
    "Copyright (c) 2019 Guillaume Chevalier. \n",
    "\n",
    "The present article's text is available under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)\n",
    "\n",
    "My source code in the present article is not available yet under a license, but it's super fine to use it for educational or research purposes as long as you cite. \n",
    "\n",
    "## Keep in touch!\n",
    "\n",
    "I frequently code open-source and source-available repositories, either from work or for fun. Also check out out my new [Machine Learning Pipeline library](https://github.com/Neuraxio/Neuraxle) for easily coding automatic machine learning pipelines. \n",
    "\n",
    "### Connect with me\n",
    "\n",
    "- [LinkedIn](https://ca.linkedin.com/in/chevalierg)\n",
    "- [Twitter](https://twitter.com/guillaume_che)\n",
    "- [GitHub](https://github.com/guillaume-chevalier/)\n",
    "- [Quora](https://www.quora.com/profile/Guillaume-Chevalier-2)\n",
    "- [YouTube](https://www.youtube.com/c/GuillaumeChevalier)\n",
    "- [Do R&D/Consulting/Products with me](http://www.neuraxio.com/en/)\n",
    "\n",
    "Liked this project? Did it help you? Leave a [star](https://github.com/guillaume-chevalier/Spiking-Neural-Networks-SNNs-with-PyTorch/stargazers), [fork](https://github.com/guillaume-chevalier/Spiking-Neural-Networks-SNNs-with-PyTorch/network/members) and share the love!\n"
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      "[NbConvertApp] Converting notebook Spiking Neural Networks with PyTorch.ipynb to markdown\n",
      "[NbConvertApp] Support files will be in Spiking Neural Networks with PyTorch_files/\n",
      "[NbConvertApp] Making directory Spiking Neural Networks with PyTorch_files\n",
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      "[NbConvertApp] Making directory Spiking Neural Networks with PyTorch_files\n",
      "[NbConvertApp] Making directory Spiking Neural Networks with PyTorch_files\n",
      "[NbConvertApp] Making directory Spiking Neural Networks with PyTorch_files\n",
      "[NbConvertApp] Making directory Spiking Neural Networks with PyTorch_files\n",
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