{ "cells": [ { "cell_type": "markdown", "id": "3f76d8bb", "metadata": {}, "source": [ "# *(Brette, et, al., 2007)* COBA\n", "\n", "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/examples/blob/main/ei_nets/Brette_2007_COBA.ipynb)\n", "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/examples/blob/main/ei_nets/Brette_2007_COBA.ipynb)" ] }, { "cell_type": "markdown", "id": "1515c9bf", "metadata": {}, "source": [ "Implementation of the paper:\n", "\n", "- Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., et al. (2007), Simulation of networks of spiking neurons: a review of tools and strategies., J. Comput. Neurosci., 23, 3, 349–98\n", "\n", "which is based on the balanced network proposed by:\n", "\n", "- Vogels, T. P. and Abbott, L. F. (2005), Signal propagation and logic gating in networks of integrate-and-fire neurons., J. Neurosci., 25, 46, 10786–95\n", "\n", "\n", "\n", "Authors:\n", "\n", "- [Chaoming Wang](https://github.com/chaoming0625)\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "44050af0", "metadata": { "ExecuteTime": { "end_time": "2023-08-27T08:09:29.427638500Z", "start_time": "2023-08-27T08:09:28.070386400Z" } }, "outputs": [], "source": [ "import brainpy as bp\n", "import brainpy.math as bm\n", "\n", "bp.math.set_platform('cpu')" ] }, { "cell_type": "code", "execution_count": 2, "id": "26ee78f4e7a4760d", "metadata": { "ExecuteTime": { "end_time": "2023-08-27T08:09:29.436707200Z", "start_time": "2023-08-27T08:09:29.428637900Z" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'2.4.4.post1'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bp.__version__" ] }, { "cell_type": "markdown", "id": "5cc9327d", "metadata": {}, "source": [ "## Version 1" ] }, { "cell_type": "code", "execution_count": 3, "id": "519fd993", "metadata": { "ExecuteTime": { "end_time": "2023-08-27T08:09:32.437604200Z", "start_time": "2023-08-27T08:09:32.423701800Z" } }, "outputs": [], "source": [ "class EINet(bp.Network):\n", " def __init__(self, scale=1.0, method='exp_auto'):\n", " # network size\n", " num_exc = int(3200 * scale)\n", " num_inh = int(800 * scale)\n", "\n", " # neurons\n", " pars = dict(V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,\n", " V_initializer=bp.init.Normal(-55., 2.))\n", " E = bp.neurons.LIF(num_exc, **pars, method=method)\n", " I = bp.neurons.LIF(num_inh, **pars, method=method)\n", "\n", " # synapses\n", " we = 0.6 / scale # excitatory synaptic weight (voltage)\n", " wi = 6.7 / scale # inhibitory synaptic weight\n", " E2E = bp.synapses.Exponential(E, E, bp.conn.FixedProb(prob=0.02),\n", " g_max=we, tau=5., method=method,\n", " output=bp.synouts.COBA(E=0.))\n", " E2I = bp.synapses.Exponential(E, I, bp.conn.FixedProb(prob=0.02),\n", " g_max=we, tau=5., method=method,\n", " output=bp.synouts.COBA(E=0.))\n", " I2E = bp.synapses.Exponential(I, E, bp.conn.FixedProb(prob=0.02),\n", " g_max=wi, tau=10., method=method,\n", " output=bp.synouts.COBA(E=-80.))\n", " I2I = bp.synapses.Exponential(I, I, bp.conn.FixedProb(prob=0.02),\n", " g_max=wi, tau=10., method=method,\n", " output=bp.synouts.COBA(E=-80.))\n", "\n", " super(EINet, self).__init__(E2E, E2I, I2E, I2I, E=E, I=I)" ] }, { "cell_type": "code", "execution_count": 4, "id": "9ad37ac1", "metadata": { "ExecuteTime": { "end_time": "2023-08-27T08:09:36.740452800Z", "start_time": "2023-08-27T08:09:32.617758300Z" } }, "outputs": [], "source": [ "# network\n", "net = EINet()" ] }, { "cell_type": "code", "execution_count": 5, "id": "793c2f64", "metadata": { "ExecuteTime": { "end_time": "2023-08-27T08:09:38.512778700Z", "start_time": "2023-08-27T08:09:36.740452800Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "905ae29b42ad452eaeacea065cf80ac0", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bp.visualize.raster_plot(runner.mon.ts, runner.mon['E.spike'], show=True)" ] }, { "cell_type": "markdown", "id": "a07e8eeb", "metadata": {}, "source": [ "## Version 2" ] }, { "cell_type": "code", "execution_count": 7, "id": "dfa45cf3", "metadata": { "ExecuteTime": { "end_time": "2023-08-27T08:09:38.990041700Z", "start_time": "2023-08-27T08:09:38.977689100Z" } }, "outputs": [], "source": [ "class EINet_V2(bp.Network):\n", " def __init__(self, scale=1.0, method='exp_auto'):\n", " super(EINet_V2, self).__init__()\n", "\n", " # network size\n", " num_exc = int(3200 * scale)\n", " num_inh = int(800 * scale)\n", "\n", " # neurons\n", " self.N = bp.neurons.LIF(num_exc + num_inh,\n", " V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,\n", " method=method, V_initializer=bp.initialize.Normal(-55., 2.))\n", "\n", " # synapses\n", " we = 0.6 / scale # excitatory synaptic weight (voltage)\n", " wi = 6.7 / scale # inhibitory synaptic weight\n", " self.Esyn = bp.synapses.Exponential(pre=self.N[:num_exc],\n", " post=self.N,\n", " conn=bp.connect.FixedProb(0.02),\n", " g_max=we, tau=5.,\n", " output=bp.synouts.COBA(E=0.),\n", " method=method)\n", " self.Isyn = bp.synapses.Exponential(pre=self.N[num_exc:],\n", " post=self.N,\n", " conn=bp.connect.FixedProb(0.02),\n", " g_max=wi, tau=10.,\n", " output=bp.synouts.COBA(E=-80.),\n", " method=method)" ] }, { "cell_type": "code", "execution_count": 8, "id": "fe47b140", "metadata": { "ExecuteTime": { "end_time": "2023-08-27T08:09:41.172929100Z", "start_time": "2023-08-27T08:09:38.986747400Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "eff327df232f4f9e8890e596b1ef5025", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net = EINet_V2(scale=1., method='exp_auto')\n", "# simulation\n", "runner = bp.DSRunner(\n", "net,\n", "monitors={'spikes': net.N.spike},\n", "inputs=[(net.N.input, 20.)]\n", ")\n", "runner.run(100.)\n", "\n", "# visualization\n", "bp.visualize.raster_plot(runner.mon.ts, runner.mon['spikes'], show=True)" ] }, { "cell_type": "markdown", "id": "56929a0af3e3f2d8", "metadata": { "collapsed": false }, "source": [ "## New Version" ] }, { "cell_type": "code", "execution_count": 9, "id": "d657476a0dbf262e", "metadata": { "ExecuteTime": { "end_time": "2023-08-27T08:09:41.172929100Z", "start_time": "2023-08-27T08:09:41.167932300Z" }, "collapsed": false }, "outputs": [], "source": [ "class EINet3(bp.DynSysGroup):\n", " def __init__(self):\n", " super().__init__()\n", " self.N = bp.dyn.LifRef(4000, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,\n", " V_initializer=bp.init.Normal(-55., 2.))\n", " self.delay = bp.VarDelay(self.N.spike, entries={'I': None})\n", " self.E = bp.dyn.ProjAlignPostMg1(comm=bp.dnn.EventJitFPHomoLinear(3200, 4000, prob=0.02, weight=0.6),\n", " syn=bp.dyn.Expon.desc(size=4000, tau=5.),\n", " out=bp.dyn.COBA.desc(E=0.),\n", " post=self.N)\n", " self.I = bp.dyn.ProjAlignPostMg1(comm=bp.dnn.EventJitFPHomoLinear(800, 4000, prob=0.02, weight=6.7),\n", " syn=bp.dyn.Expon.desc(size=4000, tau=10.),\n", " out=bp.dyn.COBA.desc(E=-80.),\n", " post=self.N)\n", "\n", " def update(self, input):\n", " spk = self.delay.at('I')\n", " self.E(spk[:3200])\n", " self.I(spk[3200:])\n", " self.delay(self.N(input))\n", " return self.N.spike.value" ] }, { "cell_type": "code", "execution_count": 10, "id": "4981a41718ef4796", "metadata": { "ExecuteTime": { "end_time": "2023-08-27T08:09:43.065579600Z", "start_time": "2023-08-27T08:09:41.171937300Z" }, "collapsed": false }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = EINet3()\n", "indices = bm.arange(1000)\n", "spks = bm.for_loop(lambda i: model.step_run(i, 20.), indices)\n", "bp.visualize.raster_plot(indices, spks, show=True)" ] } ], "metadata": { "jupytext": { "encoding": "# -*- coding: utf-8 -*-", "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.16" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 5 }