{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Conductance-based generalized leaky integrate and fire (GLIF) neuron example\n\nSimple example of how to use the ``glif_cond`` neuron model for\nfive different levels of GLIF neurons.\n\nFour stimulation paradigms are illustrated for the GLIF model\nwith externally applied current and spikes impinging\n\nVoltage traces, injecting current traces, threshold traces, synaptic\nconductance traces and spikes are shown.\n\nKEYWORDS: glif_cond\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we import all necessary modules to simulate, analyze and plot this\nexample.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import nest\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We initialize NEST and set the simulation resolution.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nest.ResetKernel()\nresolution = 0.05\nnest.resolution = resolution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create the five levels of GLIF model to be tested, i.e.,\n``lif``, ``lif_r``, ``lif_asc``, ``lif_r_asc``, ``lif_r_asc_a``.\nFor each level of GLIF model, we create a ``glif_cond`` node. The node is\ncreated by setting relative model mechanism parameters. Other neuron\nparameters are set as default. The five ``glif_cond`` node handles are\ncombined as a list. Note that the default number of synaptic ports\nis two for spike inputs. One port is excitation receptor with time\nconstant being 0.2 ms and reversal potential being 0.0 mV. The other port is\ninhibition receptor with time constant being 2.0 ms and -85.0 mV.\nNote that users can set as many synaptic ports as needed for ``glif_cond``\nby setting array parameters ``tau_syn`` and ``E_rev`` of the model.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n_lif = nest.Create(\"glif_cond\",\n params={\"spike_dependent_threshold\": False,\n \"after_spike_currents\": False,\n \"adapting_threshold\": False})\nn_lif_r = nest.Create(\"glif_cond\",\n params={\"spike_dependent_threshold\": True,\n \"after_spike_currents\": False,\n \"adapting_threshold\": False})\nn_lif_asc = nest.Create(\"glif_cond\",\n params={\"spike_dependent_threshold\": False,\n \"after_spike_currents\": True,\n \"adapting_threshold\": False})\nn_lif_r_asc = nest.Create(\"glif_cond\",\n params={\"spike_dependent_threshold\": True,\n \"after_spike_currents\": True,\n \"adapting_threshold\": False})\nn_lif_r_asc_a = nest.Create(\"glif_cond\",\n params={\"spike_dependent_threshold\": True,\n \"after_spike_currents\": True,\n \"adapting_threshold\": True})\n\nneurons = n_lif + n_lif_r + n_lif_asc + n_lif_r_asc + n_lif_r_asc_a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the stimulation input to the glif_cond neurons, we create one excitation\nspike generator and one inhibition spike generator, each of which generates\nthree spikes; we also create one step current generator and a Poisson\ngenerator, a parrot neuron(to be paired with the Poisson generator).\nThe three different injections are spread to three different time periods,\ni.e., 0 ms ~ 200 ms, 200 ms ~ 500 ms, 600 ms ~ 900 ms.\nConfiguration of the current generator includes the definition of the start\nand stop times and the amplitude of the injected current. Configuration of\nthe Poisson generator includes the definition of the start and stop times and\nthe rate of the injected spike train.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "espikes = nest.Create(\"spike_generator\",\n params={\"spike_times\": [10., 100., 150.],\n \"spike_weights\": [20.] * 3})\nispikes = nest.Create(\"spike_generator\",\n params={\"spike_times\": [15., 99., 150.],\n \"spike_weights\": [-20.] * 3})\ncg = nest.Create(\"step_current_generator\",\n params={\"amplitude_values\": [400., ],\n \"amplitude_times\": [200., ],\n \"start\": 200., \"stop\": 500.})\npg = nest.Create(\"poisson_generator\",\n params={\"rate\": 15000., \"start\": 600., \"stop\": 900.})\npn = nest.Create(\"parrot_neuron\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The generators are then connected to the neurons. Specification of\nthe ``receptor_type`` uniquely defines the target receptor.\nWe connect current generator to receptor 0, the excitation spike generator\nand the Poisson generator (via parrot neuron) to receptor 1, and the\ninhibition spike generator to receptor 2 of the GLIF neurons.\nNote that Poisson generator is connected to parrot neuron to transit the\nspikes to the glif_cond neuron.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nest.Connect(cg, neurons, syn_spec={\"delay\": resolution})\nnest.Connect(espikes, neurons,\n syn_spec={\"delay\": resolution, \"receptor_type\": 1})\nnest.Connect(ispikes, neurons,\n syn_spec={\"delay\": resolution, \"receptor_type\": 2})\nnest.Connect(pg, pn, syn_spec={\"delay\": resolution})\nnest.Connect(pn, neurons, syn_spec={\"delay\": resolution, \"receptor_type\": 1})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A ``multimeter`` is created and connected to the neurons. The parameters\nspecified for the multimeter include the list of quantities that should be\nrecorded and the time interval at which quantities are measured.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mm = nest.Create(\"multimeter\",\n params={\"interval\": resolution,\n \"record_from\": [\"V_m\", \"I\", \"g_1\", \"g_2\",\n \"threshold\",\n \"threshold_spike\",\n \"threshold_voltage\",\n \"ASCurrents_sum\"]})\nnest.Connect(mm, neurons)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A ``spike_recorder`` is created and connected to the neurons record the\nspikes generated by the glif_cond neurons.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sr = nest.Create(\"spike_recorder\")\nnest.Connect(neurons, sr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the simulation for 1000 ms and retrieve recorded data from\nthe multimeter and spike recorder.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nest.Simulate(1000.)\n\ndata = mm.events\nsenders = data[\"senders\"]\n\nspike_data = sr.events\nspike_senders = spike_data[\"senders\"]\nspikes = spike_data[\"times\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We plot the time traces of the membrane potential (in blue) and\nthe overall threshold (in green), and the spikes (as red dots) in one panel;\nthe spike component of threshold (in yellow) and the voltage component of\nthreshold (in black) in another panel; the injected currents (in strong blue),\nthe sum of after spike currents (in cyan) in the third panel; and the synaptic\nconductances of the two receptors (in blue and orange) in responding to the\nspike inputs to the neurons in the fourth panel. We plot all these four\npanels for each level of GLIF model in a separated figure.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "glif_models = [\"lif\", \"lif_r\", \"lif_asc\", \"lif_r_asc\", \"lif_r_asc_a\"]\nfor i in range(len(glif_models)):\n\n glif_model = glif_models[i]\n node_id = neurons[i].global_id\n plt.figure(glif_model)\n gs = gridspec.GridSpec(4, 1, height_ratios=[2, 1, 1, 1])\n t = data[\"times\"][senders == 1]\n\n ax1 = plt.subplot(gs[0])\n plt.plot(t, data[\"V_m\"][senders == node_id], \"b\")\n plt.plot(t, data[\"threshold\"][senders == node_id], \"g--\")\n plt.plot(spikes[spike_senders == node_id],\n [max(data[\"threshold\"][senders == node_id]) * 0.95] *\n len(spikes[spike_senders == node_id]), \"r.\")\n plt.legend([\"V_m\", \"threshold\", \"spike\"])\n plt.ylabel(\"V (mV)\")\n plt.title(\"Simulation of glif_cond neuron of \" + glif_model)\n\n ax2 = plt.subplot(gs[1])\n plt.plot(t, data[\"threshold_spike\"][senders == node_id], \"y\")\n plt.plot(t, data[\"threshold_voltage\"][senders == node_id], \"k--\")\n plt.legend([\"threshold_spike\", \"threshold_voltage\"])\n plt.ylabel(\"V (mV)\")\n\n ax3 = plt.subplot(gs[2])\n plt.plot(t, data[\"I\"][senders == node_id], \"--\")\n plt.plot(t, data[\"ASCurrents_sum\"][senders == node_id], \"c-.\")\n plt.legend([\"I_e\", \"ASCurrents_sum\", \"I_syn\"])\n plt.ylabel(\"I (pA)\")\n plt.xlabel(\"t (ms)\")\n\n ax4 = plt.subplot(gs[3])\n plt.plot(t, data[\"g_1\"][senders == node_id], \"-\")\n plt.plot(t, data[\"g_2\"][senders == node_id], \"--\")\n plt.legend([\"G_1\", \"G_2\"])\n plt.ylabel(\"G (nS)\")\n plt.xlabel(\"t (ms)\")\n\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": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 0 }