2022-11-28_Generative-model_figure.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9047cb89-b0b1-42ba-b4c0-d443983a52f2",
   "metadata": {
    "tags": []
   },
   "source": [
    "### generative & inference models for polychronous groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "72cfb669-0306-4ec4-b86a-05d3a642e5e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from brainhack import *\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1176a73f-9aaf-4581-aff1-fef7d8ca5568",
   "metadata": {},
   "source": [
    "## Figure avec un nombre réduit de neurones / synapses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "150f9909-f630-4d1e-b81a-e8df61e4fb86",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_PGs = 4\n",
    "opt_fig1 = Params(  datetag = datetag + '_fig1',\n",
    "                    N_pre = 10, \n",
    "                    N_PGs = N_PGs,\n",
    "                    N_PG_time = 71,\n",
    "                    p_PG = .07,\n",
    "                    E_PG=14.0,\n",
    "                    p_A = 0.0005,\n",
    "                    p_B = 0.001,\n",
    "                    N_PG_show = N_PGs,\n",
    "                    N_trials = 1,\n",
    "                    N_time = 240,\n",
    "                    seed = 2373,\n",
    "                    fig_width = 4.5,\n",
    "                    )\n",
    "                    \n",
    "env_fig1 = ABCD(opt_fig1)\n",
    "# a, b, PG = env_fig1.generative_model()\n",
    "PG = env_fig1.set_PG(seed=opt_fig1.seed)\n",
    "b = env_fig1.get_b(seed=opt_fig1.seed+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e4ebc802-70e0-4f34-9965-bd96a2ee629f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Params(datetag='2022-11-28_fig1', N_pre=10, N_PG_time=71, N_PGs=4, E_PG=14.0, p_PG=0.07, tau_decay=0.4, tau_rise=0.05, N_time=240, N_trials=1, p_B=0.001, p_A=0.0005, seed=2373, verbose=False, fig_width=4.5, phi=1.61803, N_PG_show=4),\n",
       " Params(datetag='2022-11-28_fig1', N_pre=10, N_PG_time=71, N_PGs=4, E_PG=14.0, p_PG=0.07, tau_decay=0.4, tau_rise=0.05, N_time=240, N_trials=1, p_B=0.001, p_A=0.0005, seed=2373, verbose=False, fig_width=4.5, phi=1.61803, N_PG_show=4),\n",
       " 10,\n",
       " 4)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt_fig1, env_fig1.opt, env_fig1.opt.N_pre, env_fig1.opt.N_PGs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fd2a317e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4, 71)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env_fig1.opt.N_pre, env_fig1.opt.N_PGs, env_fig1.opt.N_PG_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d85f4976-f188-4fb5-87da-f133ee654fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "subplotpars_fig1 = matplotlib.figure.SubplotParams(left=0., right=1., bottom=0., top=1., wspace=0.08, hspace=0.05,)\n",
    "figsize = (env_fig1.opt.fig_width, env_fig1.opt.fig_width/env_fig1.opt.phi)\n",
    "colors = ['r', 'g' , 'b' , 'm']\n",
    "shift = -0.23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1d0f5ab4-ad12-4570-bc5e-b43fa9ab5950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 4, 240])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = torch.zeros(b.shape)\n",
    "b[0, 0, 50] = True\n",
    "b[0, 1, 201] = True\n",
    "b[0, 1, 114] = True\n",
    "b[0, 2, 42] = True\n",
    "b[0, 2, 104] = True\n",
    "b[0, 3, 180] = True\n",
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0ab69510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 4, 71])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PG.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d57b892f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(0.000), tensor(25.070), tensor(-20.056), tensor(25.070))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PG.mean(), PG.abs().max(), PG.min(), PG.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f6efc96e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'figures'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "27d211ce-660c-4465-9856-dfa6b856f136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x278.116 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = env_fig1.plot_PG(PG, colors=colors)\n",
    "fig.text(shift, 1.05,'(b)', horizontalalignment='center', verticalalignment='center', weight='bold')\n",
    "if not figpath is None: printfig(fig, 'figure_motifs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c45e47f9-3924-4bc5-8aca-588d4c8bbb29",
   "metadata": {},
   "source": [
    "Then from the following raster plot, each PG described if the figure above will be selective to the spatio-temporal pattern of the same color: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b2eb570d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x278.116 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = env_fig1.plot_b(b, colors=colors, yticks=env_fig1.opt.N_PGs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6b9b9bab-3c77-4aad-9fb5-59a52b842112",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x278.116 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = env_fig1.plot_b(b, colors=colors, figsize=figsize, subplotpars=subplotpars_fig1, yticks=env_fig1.opt.N_PGs)\n",
    "fig.text(shift, 0.95,'(c)', horizontalalignment='center', verticalalignment='center', weight='bold')\n",
    "\n",
    "if not figpath is None: printfig(fig, 'figure_raster_motifs')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "262af743",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = env_fig1.draw_a(b, PG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "872b6402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x278.116 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x278.116 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for text in ['before', 'after']:\n",
    "    fig, ax = env_fig1.plot_a(a, b, PG, colors=colors if text=='after' else ['k'], figsize=figsize, subplotpars=subplotpars_fig1, yticks=env_fig1.opt.N_pre)\n",
    "    \n",
    "    fig.text(-0.15, 0.95, text, horizontalalignment='center', verticalalignment='center', weight='bold')\n",
    "    printfig(fig, 'figure_motif_' + text)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3492eeed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('brainhack')",
   "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": "3.9.13"
  },
  "toc-autonumbering": true,
  "vscode": {
   "interpreter": {
    "hash": "f76abcfae7407d101ef34ba86f468988314045e4e2c214982f718a8acd3606b2"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}