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        "# Classification of a single digit\n",
        "In this series of tutorial, we are going to build an SNN capable of classifying MNIST by copying the weights obtained by training the following simple ANN using TensorFlow:\n",
        "\n",
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       fill="#000000" />
  </g>
</svg>
)\n",
        "\n",
        "Clearly, this is far from a state of the art architecture, but it still achieves 97.6% accuracy on MNIST. In this first tutorial we are going to build the basic SNN model, present a single test set image to it and visualize the spiking activitiy of the model.\n",
        "\n",
        "## Install PyGeNN wheel from Google Drive\n",
        "Download wheel file"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if \"google.colab\" in str(get_ipython()):\n",
        "    #import IPython\n",
        "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
        "    #%run \"../install_collab.ipynb\"\n",
        "    !pip install gdown --upgrade\n",
        "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
        "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
        "    %env CUDA_PATH=/usr/local/cuda"
      ],
      "metadata": {
        "id": "VXltW7HVbtcj",
        "outputId": "10383607-e3f3-41b4-9751-b80d3a858cbc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
            "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
            "100% 8.29M/8.29M [00:00<00:00, 147MB/s]\n",
            "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
            "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
            "env: CUDA_PATH=/usr/local/cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8tqbF5GldF0o"
      },
      "source": [
        "## Download pre-trained weights and MNIST test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-2PV7LcdFg_",
        "outputId": "177625f2-5aa4-4b98-8fec-aafd20520cdb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
            "To: /content/weights_0_1.npy\n",
            "100% 402k/402k [00:00<00:00, 142MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF\n",
            "To: /content/weights_1_2.npy\n",
            "100% 5.25k/5.25k [00:00<00:00, 18.8MB/s]\n"
          ]
        }
      ],
      "source": [
        "!gdown 1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
        "!gdown 131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install MNIST package"
      ],
      "metadata": {
        "id": "KVRtXVzIg07T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mnist"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AikBc4sfg1b-",
        "outputId": "bb469225-f242-4f8f-c1ef-e997d97d2066"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting mnist\n",
            "  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n",
            "Installing collected packages: mnist\n",
            "Successfully installed mnist-0.2.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwadUz9Azxss"
      },
      "source": [
        "## Build model\n",
        "Import standard modules and required PyGeNN functions and classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "agqWFZjickfU"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import mnist\n",
        "import matplotlib.pyplot as plt\n",
        "from pygenn import (create_neuron_model, create_current_source_model,\n",
        "                    init_postsynaptic, init_weight_update, GeNNModel)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iuwdL6IE2MuS"
      },
      "source": [
        "Define some simulation parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C68EDXn6cj-O"
      },
      "outputs": [],
      "source": [
        "# Simulation timestep of model in ms\n",
        "TIMESTEP = 1.0\n",
        "\n",
        "# How many timesteps to present images for\n",
        "PRESENT_TIMESTEPS = 100\n",
        "\n",
        "# How much to scale input images\n",
        "INPUT_CURRENT_SCALE = 1.0 / 100.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9PCMgNPCz86O"
      },
      "source": [
        "Because the ReLU neurons our ANN was trained with are best matched by a very simple Integrate-and-Fire neuron without a leak, define a custom model with:\n",
        "*   A single parameter (parameters are common across all neurons in population) `Vthr` which specifies it's spiking threshold\n",
        "*   A single `V` state variable to hold the membrane potential of the neurons\n",
        "*   Simulation code which simply adds the incoming current `Isyn` (this is a built in variable provided by GeNN) to the membrane potential `V` (note, we're assuming that the membrane resistance is 1 here)\n",
        "*   Threshold condition code which causes the neuron to emit a spike if it's membrane potential `V` crosses the threshold `Vthr`\n",
        "*   Reset code which zeros the membrane potential `V` after a spike is emitted."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-7lzXzmQcgbt"
      },
      "outputs": [],
      "source": [
        "if_model = create_neuron_model(\n",
        "    \"if_model\",\n",
        "    params=[\"Vthr\"],\n",
        "    vars=[(\"V\", \"scalar\")],\n",
        "    sim_code=\"V += Isyn;\",\n",
        "    threshold_condition_code=\"V >= Vthr\",\n",
        "    reset_code=\"\"\"\n",
        "    V = 0.0;\n",
        "    \"\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATwobw4Mw2LG"
      },
      "source": [
        "We are going to convert MNIST digits to spikes by simply treating the intensity of each pixel (multiplied by a scaling factor) as a current and injecting it into the neurons in the input population throughout the stimulus presentation time.\n",
        "\n",
        "To do this we use a very simple custom current source model with:\n",
        "\n",
        "*   A single `magnitude` state variable to store the per-neuron current to inject\n",
        "*   Injection code which injects a current of `magnitude` every timestep (`$(injectCurrent, X)` is a function provided by GeNN for use in current sources).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EEQyoL-zcu-A"
      },
      "outputs": [],
      "source": [
        "cs_model = create_current_source_model(\n",
        "    \"cs_model\",\n",
        "    vars=[(\"magnitude\", \"scalar\")],\n",
        "    injection_code=\"injectCurrent(magnitude);\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TnawdZkzyJQZ"
      },
      "source": [
        "Create a new model implementing `scalar` variables as single-precision and generating code into tutorial_1 directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J1VY795eeFa8"
      },
      "outputs": [],
      "source": [
        "model = GeNNModel(\"float\", \"tutorial_1\")\n",
        "model.dt = TIMESTEP"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p4wFWaMS1M8_"
      },
      "source": [
        "\n",
        "Load the weight matrices extracted from our original ANN\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6RVz8FPmc4n5"
      },
      "outputs": [],
      "source": [
        "# Load weights\n",
        "weights_0_1 = np.load(\"weights_0_1.npy\")\n",
        "weights_1_2 = np.load(\"weights_1_2.npy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ik9NVB3-1Mdy"
      },
      "source": [
        "Create three populations of Integrate-and-Fire neurons sized to match the shapes of the weight matrices and initialised so their membrane potential's are all initialised to 0mv and their spiking thresholds to 5mv."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ODYWtS28cxGi"
      },
      "outputs": [],
      "source": [
        "if_init = {\"V\": 0.0}\n",
        "if_params = {\"Vthr\": 5.0}\n",
        "neurons = [model.add_neuron_population(\"neuron0\", weights_0_1.shape[0],\n",
        "                                       if_model, if_params, if_init),\n",
        "           model.add_neuron_population(\"neuron1\", weights_0_1.shape[1],\n",
        "                                       if_model, if_params, if_init),\n",
        "           model.add_neuron_population(\"neuron2\", weights_1_2.shape[1],\n",
        "                                       if_model, if_params, if_init)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DPD-YOP81dGN"
      },
      "source": [
        "Because, in this first tutorial we want to examine the spike emitted by each neuron, turn on spike recording for each population."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "elcdezDSeTe4"
      },
      "outputs": [],
      "source": [
        "for n in neurons:\n",
        "    n.spike_recording_enabled = True"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fj3wbKso1j2d"
      },
      "source": [
        "Add synapse populations to sequentially connect the three populations of neurons. These are all configured identically with:\n",
        "*   `DENSE` connectivity meaning that they are connected with a basic dense weight matrix(see [documentation](https://genn-team.github.io/genn/documentation/4/html/d5/d39/subsect34.html)).\n",
        "*   The built in `StaticPulse` **weight update model** which is used for spiking synapses without any sort of learning. This has no parameters and a single state variable `g` representing its synaptic weights which we initialise using our arrays of pre-trained weights.\n",
        "*   The build in `DeltaCurr` **postsynaptic model** which specified that weighted incoming spikes are added directly to the postsynaptic neuron's membrane potential without any additional shaping occuring. This model has no parameters or state variables.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sx1VOU5udixG"
      },
      "outputs": [],
      "source": [
        "model.add_synapse_population(\n",
        "        \"synapse_0_1\", \"DENSE\",\n",
        "        neurons[0], neurons[1],\n",
        "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_0_1.flatten()}),\n",
        "        init_postsynaptic(\"DeltaCurr\"))\n",
        "model.add_synapse_population(\n",
        "        \"synapse_1_2\", \"DENSE\",\n",
        "        neurons[1], neurons[2],\n",
        "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_1_2.flatten()}),\n",
        "        init_postsynaptic(\"DeltaCurr\"));"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQaIR8-ByoSI"
      },
      "source": [
        "Add current source to provide input into the input population of neurons"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yENqkr6KeMLp"
      },
      "outputs": [],
      "source": [
        "current_input = model.add_current_source(\"current_input\", cs_model,\n",
        "                                         neurons[0], {}, {\"magnitude\": 0.0})\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "38pYD7rxytBT"
      },
      "source": [
        "Run code generator to generate simulation code for model and load it into PyGeNN. Allocate a spike recording buffer large enough to store the spikes emitted throughout a single stimuli presentation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0Tf07KUOeP-X"
      },
      "outputs": [],
      "source": [
        "model.build()\n",
        "model.load(num_recording_timesteps=PRESENT_TIMESTEPS)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vBHoR-Eu2r6R"
      },
      "source": [
        "## Simulate model\n",
        "First we load the two numpy arrays containing the images and labels from the MNIST test set and verify that the size of the input images matches that of the first (input) population and that the size of the last (output) population is enough to one-hot encode all of the labels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qVWFKwiRehF8"
      },
      "outputs": [],
      "source": [
        "mnist.datasets_url = \"https://storage.googleapis.com/cvdf-datasets/mnist/\"\n",
        "testing_images = mnist.test_images()\n",
        "testing_labels = mnist.test_labels()\n",
        "\n",
        "testing_images = np.reshape(testing_images, (testing_images.shape[0], -1))\n",
        "assert testing_images.shape[1] == neurons[0].num_neurons\n",
        "assert np.max(testing_labels) == (neurons[-1].num_neurons - 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t-F5qx030CdT"
      },
      "source": [
        "PyGeNN uses *memory views* to directly expose the memory used by the simulation to numpy. Copy the first testing image into the memory view of the current source's magnitude variable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3z1ccKHeejeB"
      },
      "outputs": [],
      "source": [
        "current_input.vars[\"magnitude\"].values = testing_images[0] * INPUT_CURRENT_SCALE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U06Zwnuk0ihm"
      },
      "source": [
        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)\n",
        "\n",
        "On most systems, memory accessible by the GPU and the CPU is seperate.\n",
        "Therefore, we need to manually copy the values we just placed in the current source's magnitude variable to the GPU (if we're running GeNN on the CPU, this call will not do anything)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OfdQXEtd0jRi"
      },
      "outputs": [],
      "source": [
        "current_input.vars[\"magnitude\"].push_to_device()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OfUYJwC13VOk"
      },
      "source": [
        "Simulate the model for `PRESENT_TIMESTEPS` (`model.timestep` tracks integer timesteps whereas `model.time` tracks time in ms)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4qSoinT4etKq"
      },
      "outputs": [],
      "source": [
        "while model.timestep < PRESENT_TIMESTEPS:\n",
        "    model.step_time()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Xg5AItK3ahj"
      },
      "source": [
        "Download the recorded spikes from the GPU"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wOhN-Qcuexjy"
      },
      "outputs": [],
      "source": [
        "model.pull_recording_buffers_from_device()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dwGQ4ygO3f2b"
      },
      "source": [
        "Plot raster plots of the spikes from all neuron populations, illustrating the correct label for this image with a horizontal line"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "dBFluY10e7Ba",
        "outputId": "fd82e034-e5ad-4c3b-cbd2-e998429cfab1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create figure with one axis per neuron population\n",
        "fig, axes = plt.subplots(len(neurons), sharex=True)\n",
        "\n",
        "# Loop through neuron populations and the axis we're going to plot their raster plot on\n",
        "for n, a in zip(neurons, axes):\n",
        "    # Extract spike times and IDs and plot\n",
        "    spike_times, spike_ids = n.spike_recording_data[0]\n",
        "    a.scatter(spike_times, spike_ids, s=1)\n",
        "\n",
        "    a.set_title(n.name)\n",
        "    a.set_ylabel(\"Neuron ID\")\n",
        "    a.set_xlim((0, PRESENT_TIMESTEPS * TIMESTEP))\n",
        "    a.set_ylim((0, n.num_neurons))\n",
        "\n",
        "# Add an x-axis label and translucent line showing the correct label\n",
        "axes[-1].set_xlabel(\"Time [ms]\")\n",
        "axes[-1].hlines(testing_labels[0], xmin=0, xmax=PRESENT_TIMESTEPS,\n",
        "                linestyle=\"--\", color=\"gray\", alpha=0.2);"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "tutorial_1",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}