{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/gp1514/.pyenv/versions/anaconda3-4.3.0/lib/python3.6/site-packages/matplotlib/__init__.py:1405: UserWarning: \n", "This call to matplotlib.use() has no effect because the backend has already\n", "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", "\n", " warnings.warn(_use_error_msg)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "********************************************************************************\n", "functionsTF loaded!\n", "********************************************************************************\n" ] } ], "source": [ "%matplotlib inline\n", "from fns import *\n", "from fns.functionsTF import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 800 200\n", "1000 800 200\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 5000/5000 [00:29<00:00, 172.18it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "29.36\n", "\n", "1000 800 200\n", "1000 800 200\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 5000/5000 [00:28<00:00, 174.34it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "28.95\n", "\n" ] } ], "source": [ "config = load_config()\n", "\n", "res = []\n", "T = 5000\n", "\n", "# run two simulations, first without shared gap junctions, then with 40 shared gap junctions\n", "for sG in [0,40]:\n", " gpu = TfConnEvolveNet(config=config, T=T)\n", "\n", " # number of cross-network gap junctions\n", " gpu.sG = sG\n", "\n", " NE = 800\n", " NI = 200\n", " ## input network\n", " # number of excitatory neurons\n", " gpu.NE1=NE\n", " # number of inhibitory neurons\n", " gpu.NI1=NI\n", "\n", " ## gamma network\n", " # number of excitatory neurons\n", " gpu.NE2=NE\n", " # number of inhibitory neurons\n", " gpu.NI2=NI\n", "\n", " ### input to the input-network\n", " seed = 0\n", " # input amplitude\n", " A = 400\n", " x = generateInput2(seed, T, tau=100)[np.newaxis, :]\n", " w0 = np.random.rand(1, 2*(NE+NI)) * 2\n", " w0 = w0 * A\n", " w0 = w0 * np.concatenate([np.ones((1, NE+NI)), np.zeros((1, NE+NI))], axis=1)\n", " INP = w0.T @ x + 200\n", "\n", " ### small constant drive to the output-network\n", " # k = np.ones((1, T)) * 200\n", " # w1 = np.concatenate([np.zeros((1, NE+NI)), np.ones((1, NE+NI))], axis=1)\n", " # INP2 = w1.T @ k\n", "\n", " # feed input to network\n", " gpu.input = INP \n", "\n", " # choose hardware\n", " gpu.device = '/gpu:0'\n", "\n", " # mean initial gap junction coupling\n", " gpu.g1 = 5.5\n", " gpu.g2 = 5.5\n", "\n", " # do not save the spikes\n", " gpu.spikeMonitor = False\n", " # do not save the individual voltages, currents, etc.\n", " gpu.monitor_single = False\n", "\n", " # iteration \n", " gpu.stabTime = np.inf\n", "\n", " # rule: g0 = 0 for no bound rule, g0 = 10 for softbound rule\n", " gpu.g0 = 10\n", "\n", " gpu.runTFSimul()\n", "\n", " res.append(gpu)\n", " del gpu\n", " gc.collect()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Reconstruction input" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.387380020334\n", "0.921937687734\n" ] }, { "data": { "image/png": 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+YlLKjURecQUJD5Azb1XET2XSJtL5lWhyt0zaiB6hFAiE8fvfH9K0j5BCObmB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\n9enaryih+bVvuEH9Pmrmp7lcwMmTyp//0z+RkTc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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff2c0739a58>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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76hh6l672PZpH7/X3vifdfv315H/hpm/JBcjt75AUYd23yMBEKcJIREmjAZqa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\ncPvta6HVxvucaaHRaLBsmQEWiw0mb4UyMgSnTLE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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff2c067f4a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s = 0\n", "e = T\n", "\n", "for i in range(2):\n", " f(5,4)\n", " # input signal to input-network\n", " inp = res[i].input[0,s:e]/np.max(res[i].input[0,s:e])\n", " # population activity of inhibitory neurons of the output-network\n", " ifr = res[i].vvmI2[s:e] \n", "\n", " spikes, xdec, ydec, corr_predict = decode(inp, ifr )\n", " \n", " # rescale signals for plotting\n", " inp -= np.min(inp)\n", " inp /= np.max(inp)\n", " ydec -= np.min(ydec)\n", " ydec /= np.max(ydec)\n", " \n", " # plot input\n", " plt.plot(xdec, inp, color='r', label='input')\n", " # plot decoded input\n", " plt.plot(xdec,ydec, color=snCol, label='decoded input')\n", " plt.xticks([])\n", " plt.yticks([])\n", " plt.legend(fontsize=18, loc='best', handlelength=1)\n", " plt.xlabel('Time [%d ms]'%int((e-s)*res[i].dt))\n", " plt.title('Input vs Decoded Input')\n", " if i==0:\n", " plt.suptitle(r'\\textbf{No cross-network GJs}', y=1.05, fontsize=22)\n", " else:\n", " plt.suptitle(r'\\textbf{40 cross-network GJs}', y=1.05, fontsize=22)\n", "\n", " print(np.corrcoef(ydec,res[i].input[0,s:e])[0,1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }