{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Network of linear rate neurons\n\nThis script simulates an excitatory and an inhibitory population\nof ``lin_rate_ipn`` neurons with delayed excitatory and instantaneous\ninhibitory connections. The rate of all neurons is recorded using\na multimeter. The resulting rate for one excitatory and one\ninhibitory neuron is plotted.\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import nest\nimport numpy\nimport matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assigning the simulation parameters to variables.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dt = 0.1 # the resolution in ms\nT = 100.0 # Simulation time in ms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Definition of the number of neurons\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "order = 50\nNE = int(4 * order) # number of excitatory neurons\nNI = int(1 * order) # number of inhibitory neurons\nN = int(NE+NI) # total number of neurons" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Definition of the connections\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d_e = 5. # delay of excitatory connections in ms\ng = 5.0 # ratio inhibitory weight/excitatory weight\nepsilon = 0.1 # connection probability\nw = 0.1/numpy.sqrt(N) # excitatory connection strength\n\nKE = int(epsilon * NE) # number of excitatory synapses per neuron (outdegree)\nKI = int(epsilon * NI) # number of inhibitory synapses per neuron (outdegree)\nK_tot = int(KI + KE) # total number of synapses per neuron\nconnection_rule = 'fixed_outdegree' # connection rule" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Definition of the neuron model and its neuron parameters\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "neuron_model = 'lin_rate_ipn' # neuron model\nneuron_params = {'linear_summation': True,\n # type of non-linearity (not affecting linear rate models)\n 'tau': 10.0,\n # time constant of neuronal dynamics in ms\n 'mu': 2.0,\n # mean input\n 'sigma': 5.\n # noise parameter\n }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Configuration of the simulation kernel by the previously defined time\nresolution used in the simulation. Setting ``print_time`` to True prints\nthe already processed simulation time as well as its percentage of the\ntotal simulation time.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nest.ResetKernel()\nnest.SetKernelStatus({\"resolution\": dt, \"use_wfr\": False,\n \"print_time\": True,\n \"overwrite_files\": True})\n\nprint(\"Building network\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Configuration of the neuron model using ``SetDefaults``.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nest.SetDefaults(neuron_model, neuron_params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creation of the nodes using ``Create``.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n_e = nest.Create(neuron_model, NE)\nn_i = nest.Create(neuron_model, NI)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To record from the rate neurons a ``multimeter`` is created and the parameter\n``record_from`` is set to `rate` as well as the recording interval to `dt`\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mm = nest.Create('multimeter', params={'record_from': ['rate'],\n 'interval': dt})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Specify synapse and connection dictionaries:\nConnections originating from excitatory neurons are associated\nwith a delay `d` (``rate_connection_delayed``).\nConnections originating from inhibitory neurons are not associated\nwith a delay (``rate_connection_instantaneous``).\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "syn_e = {'weight': w, 'delay': d_e, 'synapse_model': 'rate_connection_delayed'}\nsyn_i = {'weight': -g*w, 'synapse_model': 'rate_connection_instantaneous'}\nconn_e = {'rule': connection_rule, 'outdegree': KE}\nconn_i = {'rule': connection_rule, 'outdegree': KI}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Connect rate units\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nest.Connect(n_e, n_e, conn_e, syn_e)\nnest.Connect(n_i, n_i, conn_i, syn_i)\nnest.Connect(n_e, n_i, conn_i, syn_e)\nnest.Connect(n_i, n_e, conn_e, syn_i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Connect recording device to rate units\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nest.Connect(mm, n_e+n_i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simulate the network\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nest.Simulate(T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot rates of one excitatory and one inhibitory neuron\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = mm.events\nrate_ex = data['rate'][numpy.where(data['senders'] == n_e[0].global_id)]\nrate_in = data['rate'][numpy.where(data['senders'] == n_i[0].global_id)]\ntimes = data['times'][numpy.where(data['senders'] == n_e[0].global_id)]\n\nplt.figure()\nplt.plot(times, rate_ex, label='excitatory')\nplt.plot(times, rate_in, label='inhibitory')\nplt.xlabel('time (ms)')\nplt.ylabel('rate (a.u.)')\nplt.show()" ] } ], "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": "3.6.12" } }, "nbformat": 4, "nbformat_minor": 0 }