2022-11-28_Generative-model-motifs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f75d50cc-b9bd-4729-ad8c-1573df1c0546",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to install all dependencies use, uncomment the following line and restart the kernel\n",
    "# %pip install -U -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b959c09c-3907-46cd-8c06-303c59af8597",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Generative model for the superposition of spiking motifs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf094a22",
   "metadata": {},
   "source": [
    "We design a model based on heterosynaptic delays. We will define in the assembly of neurons where each synapse\n",
    "is defined by a weight *ws* at different delays *τs*. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9047cb89-b0b1-42ba-b4c0-d443983a52f2",
   "metadata": {
    "tags": []
   },
   "source": [
    "### generative & inference models for polychronous groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b8daaf2b-0466-411b-a6ba-6b5f785d102a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Params(datetag='2022-11-28', N_pre=144, N_PG_time=71, N_PGs=35, E_PG=10.0, p_PG=0.005, tau_decay=0.4, tau_rise=0.05, N_time=1024, N_trials=10, p_B=0.001, p_A=0.001, seed=42, verbose=False, fig_width=12, phi=1.61803, N_PG_show=5)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from brainhack import *\n",
    "opt = Params()\n",
    "opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1f0ab6b7-7f43-4c56-80e3-bf57b3805931",
   "metadata": {},
   "outputs": [],
   "source": [
    "env = ABCD(opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a607b61d-87de-426e-aa45-e4f314a28ef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Params(datetag='2022-11-28', N_pre=144, N_PG_time=71, N_PGs=35, E_PG=10.0, p_PG=0.005, tau_decay=0.4, tau_rise=0.05, N_time=1024, N_trials=10, p_B=0.001, p_A=0.001, seed=42, verbose=False, fig_width=12, phi=1.61803, N_PG_show=5)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.opt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc7e8553-cf29-4c78-a585-8e669a65b84e",
   "metadata": {},
   "source": [
    "## spiking motifs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "45cf7c59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, torch.Size([35, 35, 3]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(env.spike)//2, env.spike.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e70f21d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "env = ABCD(opt)\n",
    "a, b, PG = env.generative_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "54160c9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(0.), tensor(1.))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.min(), a.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "438b3711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([144, 35, 71])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.temporal_mod.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f65f41d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([144, 35, 71])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PG.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "72d580f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x297b872b0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmax = PG[0, :, :].abs().max()\n",
    "plt.imshow(PG[0, :, :], cmap='seismic', vmin=-vmax, vmax=vmax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f59873-584d-43ad-a1e5-84172faee5e5",
   "metadata": {
    "tags": []
   },
   "source": [
    "# generative model with spiking motifs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3466fd28-1975-45d1-ad10-7d604318bb3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = Params()\n",
    "env = ABCD(opt)\n",
    "a, b, PG = env.generative_model()\n",
    "# env.plot_PG(PG);\n",
    "# TODO find a better rule to generate PGs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb1eb03-3ee1-4599-bb89-5d769ce9d872",
   "metadata": {},
   "source": [
    "Draw one instance of single PGs:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22abf88b-b2f9-4b16-af19-b7ca6aba7035",
   "metadata": {},
   "source": [
    "Draw the occurrences of PGs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "09b07bf1-68db-46e3-b3ed-bc41f2aec49f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x741.643 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "env.plot_b(b);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13bed7c5-824c-44e2-ad33-1508ebda8106",
   "metadata": {},
   "source": [
    "Resulting raster plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c4b278db-9cca-4d21-8428-170e1b61daf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x741.643 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "env.plot_a(a, i_trial=7);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9c31bb02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(0.007),\n",
       " tensor(0.001),\n",
       " tensor([0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.000, 0.001, 0.000, 0.001, 0.001, 0.000, 0.001, 0.001, 0.001, 0.001,\n",
       "         0.001, 0.000, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.000, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001]))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.mean(), b.mean(), b.mean(axis=(0, -1))"
   ]
  }
 ],
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