{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGa0_oLb61zz"
      },
      "source": [
        "# Feedback-inhibition based gain control\n",
        "Based on the highly variable levels of Kenyon Cell activity found in the last tutorial, here we add feedback inhibition inspired by the Giant GABAergic Neuron (GGN) found in Drosophila and, with this in place, visualize the spiking activity of Kenyon Cells in response to latency coded MNIST digits.\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": "Ki3IZh5Jij4W",
        "outputId": "efd99f19-ea69-4061-8012-f03535ca2715",
        "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",
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            "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, 82.3MB/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",
      "source": [
        "## Install MNIST package"
      ],
      "metadata": {
        "id": "KVRtXVzIg07T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mnist"
      ],
      "metadata": {
        "id": "AikBc4sfg1b-",
        "outputId": "d201ba31-0261-408c-b461-b3d7207c03ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mnist in /usr/local/lib/python3.10/dist-packages (0.2.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yV0JrchrfQKR"
      },
      "source": [
        "## Build tutorial model\n",
        "Import modules"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hl53yKXi9LiV"
      },
      "outputs": [],
      "source": [
        "import mnist\n",
        "import numpy as np\n",
        "from copy import copy\n",
        "from matplotlib import pyplot as plt\n",
        "from pygenn import (create_current_source_model, create_neuron_model, init_postsynaptic,\n",
        "                    init_sparse_connectivity, init_weight_update, GeNNModel)\n",
        "\n",
        "# Reshape and normalise training data\n",
        "mnist.datasets_url = \"https://storage.googleapis.com/cvdf-datasets/mnist/\"\n",
        "training_images = mnist.train_images()\n",
        "training_images = np.reshape(training_images, (training_images.shape[0], -1)).astype(np.float32)\n",
        "training_images /= np.sum(training_images, axis=1)[:, np.newaxis]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0IfyML59Lif"
      },
      "source": [
        "## Parameters\n",
        "Define some model parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oncGyriW9Lif"
      },
      "outputs": [],
      "source": [
        "# Simulation time step\n",
        "DT = 0.1\n",
        "\n",
        "# Scaling factor for converting normalised image pixels to input currents (nA)\n",
        "INPUT_SCALE = 80.0\n",
        "\n",
        "# Number of Projection Neurons in model (should match image size)\n",
        "NUM_PN = 784\n",
        "\n",
        "# Number of Kenyon Cells in model (defines memory capacity)\n",
        "NUM_KC = 20000\n",
        "\n",
        "# How long to present each image to model\n",
        "PRESENT_TIME_MS = 20.0\n",
        "\n",
        "# Standard LIF neurons parameters\n",
        "LIF_PARAMS = {\n",
        "    \"C\": 0.2,\n",
        "    \"TauM\": 20.0,\n",
        "    \"Vrest\": -60.0,\n",
        "    \"Vreset\": -60.0,\n",
        "    \"Vthresh\": -50.0,\n",
        "    \"Ioffset\": 0.0,\n",
        "    \"TauRefrac\": 2.0}\n",
        "\n",
        "# We only want PNs to spike once\n",
        "PN_PARAMS = copy(LIF_PARAMS)\n",
        "PN_PARAMS[\"TauRefrac\"] = 100.0\n",
        "\n",
        "# Weight of each synaptic connection\n",
        "PN_KC_WEIGHT = 0.2\n",
        "\n",
        "# Time constant of synaptic integration\n",
        "PN_KC_TAU_SYN = 3.0\n",
        "\n",
        "# How many projection neurons should be connected to each Kenyon Cell\n",
        "PN_KC_FAN_IN = 20"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KldVFE9dJdv8"
      },
      "source": [
        "As we're now going to be adding our synaptic connections between the Projection Neurons and a new population of Kenyon Cells, also define some parameter for these"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZvNwgTphJeM9"
      },
      "outputs": [],
      "source": [
        "# We will use weights of 1.0 for KC->GGN connections and\n",
        "# want the GGN to inhibit the KCs after 200 spikes\n",
        "GGN_PARAMS = {\n",
        "    \"Vthresh\": 200.0}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCYjAoJf9Lig"
      },
      "source": [
        "## Custom models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IR8PXBg69Lih"
      },
      "outputs": [],
      "source": [
        "# Current source model, allowing current to be injected into neuron from variable\n",
        "cs_model = create_current_source_model(\n",
        "    \"cs_model\",\n",
        "    vars=[(\"magnitude\", \"scalar\")],\n",
        "    injection_code=\"injectCurrent(magnitude);\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pe-5DQ9hezIs"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Minimal integrate and fire neuron model\n",
        "if_model = create_neuron_model(\n",
        "    \"IF\",\n",
        "    params=[\"Vthresh\"],\n",
        "    vars=[(\"V\", \"scalar\")],\n",
        "    sim_code=\n",
        "    \"\"\"\n",
        "    V += Isyn;\n",
        "    \"\"\",\n",
        "    threshold_condition_code=\n",
        "    \"\"\"\n",
        "    V >= Vthresh\n",
        "    \"\"\",\n",
        "    reset_code=\n",
        "    \"\"\"\n",
        "    V = 0.0;\n",
        "    \"\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gn4DpkPQ9Lii"
      },
      "source": [
        "## Model definition\n",
        "Create a new model called \"mnist_mb_second_layer_gain_control\" as before but add a second population of `NUM_KC` neurons to represent the Kenyon Cells."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gx-GsJhD9Lik"
      },
      "outputs": [],
      "source": [
        "# Create model\n",
        "model = GeNNModel(\"float\", \"mnist_mb_second_layer_gain_control\")\n",
        "model.dt = DT\n",
        "\n",
        "# Create neuron populations\n",
        "lif_init = {\"V\": PN_PARAMS[\"Vreset\"], \"RefracTime\": 0.0}\n",
        "pn = model.add_neuron_population(\"pn\", NUM_PN, \"LIF\", PN_PARAMS, lif_init)\n",
        "kc = model.add_neuron_population(\"kc\", NUM_KC, \"LIF\", LIF_PARAMS, lif_init)\n",
        "\n",
        "# Turn on spike recording\n",
        "pn.spike_recording_enabled = True\n",
        "kc.spike_recording_enabled = True\n",
        "\n",
        "# Create current sources to deliver input to network\n",
        "pn_input = model.add_current_source(\"pn_input\", cs_model, pn , {}, {\"magnitude\": 0.0})\n",
        "\n",
        "# Create synapse populations\n",
        "pn_kc = model.add_synapse_population(\"pn_kc\", \"SPARSE\",\n",
        "                                     pn, kc,\n",
        "                                     init_weight_update(\"StaticPulseConstantWeight\", {\"g\": PN_KC_WEIGHT}),\n",
        "                                     init_postsynaptic(\"ExpCurr\", {\"tau\": PN_KC_TAU_SYN}),\n",
        "                                     init_sparse_connectivity(\"FixedNumberPreWithReplacement\", {\"num\": PN_KC_FAN_IN}))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sdYo9umiH06S"
      },
      "source": [
        "Add a current source to inject current into `pn` using our newly-defined custom model with the initial magnitude set to zero."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7e1if0YCG_7m"
      },
      "outputs": [],
      "source": [
        "if_init = {\"V\": 0.0}\n",
        "ggn = model.add_neuron_population(\"ggn\", 1, if_model, GGN_PARAMS, if_init)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9wCO8tBLfLm8"
      },
      "outputs": [],
      "source": [
        "kc_ggn = model.add_synapse_population(\"kc_ggn\", \"DENSE\",\n",
        "                                      kc, ggn,\n",
        "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": 1.0}),\n",
        "                                      init_postsynaptic(\"DeltaCurr\"))\n",
        "\n",
        "ggn_kc = model.add_synapse_population(\"ggn_kc\", \"DENSE\",\n",
        "                                      ggn, kc,\n",
        "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": -5.0}),\n",
        "                                      init_postsynaptic(\"ExpCurr\", {\"tau\": 5.0}))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-GU4oXOS9Lil"
      },
      "source": [
        "## Build model\n",
        "Generate code and load it into PyGeNN allocating a large enough spike recording buffer to cover `PRESENT_TIME_MS` (after converting from ms to timesteps)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-FE02Zoz9Lim"
      },
      "outputs": [],
      "source": [
        "# Concert present time into timesteps\n",
        "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
        "\n",
        "# Build model and load it\n",
        "model.build()\n",
        "model.load(num_recording_timesteps=present_timesteps)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CcpTaaB39Lim"
      },
      "source": [
        "## Simulate tutorial model\n",
        "As well as resetting the state of every neuron after presenting each stimuli, because we have now added synapses with their own dynamics, these also need to be reset.\n",
        " This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DfcqDTVXdoRq"
      },
      "source": [
        "Now, like before, we loop through 4 stimuli and simulate the model. However, now we need to reset the Projection Neuron and Kenyon Cell populations; **and** the synapses between them. Additionally, we want to show spikes from the Kenyon Cells as well as the Projection Neurons."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "K9pAP8OrJUub",
        "outputId": "bab547b5-0d49-4076-b996-ee47c2cb25c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "283 KC active\n",
            "272 KC active\n",
            "253 KC active\n",
            "316 KC active\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def reset_out_post(pop):\n",
        "    pop.out_post.view[:] = 0.0\n",
        "    pop.out_post.push_to_device()\n",
        "\n",
        "def reset_neuron(pop, var_init):\n",
        "    # Reset variables\n",
        "    for var_name, var_val in var_init.items():\n",
        "        pop.vars[var_name].view[:] = var_val\n",
        "\n",
        "        # Push the new values to GPU\n",
        "        pop.vars[var_name].push_to_device()\n",
        "\n",
        "for s in range(4):\n",
        "    # Set training image\n",
        "    pn_input.vars[\"magnitude\"].view[:] = training_images[s] * INPUT_SCALE\n",
        "    pn_input.vars[\"magnitude\"].push_to_device()\n",
        "\n",
        "    # Simulate present timesteps\n",
        "    for i in range(present_timesteps):\n",
        "        model.step_time()\n",
        "\n",
        "    # Reset neuron state for next stimuli\n",
        "    reset_neuron(pn, lif_init)\n",
        "    reset_neuron(kc, lif_init)\n",
        "    reset_neuron(ggn, if_init)\n",
        "\n",
        "    # Reset synapse state\n",
        "    reset_out_post(pn_kc)\n",
        "    reset_out_post(ggn_kc)\n",
        "\n",
        "    # Download spikes from GPU\n",
        "    model.pull_recording_buffers_from_device();\n",
        "\n",
        "    # Plot PN and KC spikes\n",
        "    fig, axes = plt.subplots(2, sharex=True)\n",
        "    pn_spike_times, pn_spike_ids = pn.spike_recording_data[0]\n",
        "    kc_spike_times, kc_spike_ids = kc.spike_recording_data[0]\n",
        "    print(f\"{len(np.unique(kc_spike_ids))} KC active\")\n",
        "    axes[0].scatter(pn_spike_times, pn_spike_ids, s=1)\n",
        "    axes[0].set_ylabel(\"PN\")\n",
        "    axes[1].scatter(kc_spike_times, kc_spike_ids, s=1)\n",
        "    axes[1].set_xlabel(\"Time [ms]\")\n",
        "    axes[1].set_ylabel(\"KC\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FC8WZqKZMNNM"
      },
      "source": [
        "Much better!"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "3_second_layer_gain_control",
      "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
}