# -*- coding: utf-8 -*- # # mc_neuron.py # # This file is part of NEST. # # Copyright (C) 2004 The NEST Initiative # # NEST is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # # NEST is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with NEST. If not, see . """ Multi-compartment neuron example -------------------------------- Simple example of how to use the three-compartment ``iaf_cond_alpha_mc`` neuron model. Three stimulation paradigms are illustrated: - externally applied current, one compartment at a time - spikes impinging on each compartment, one at a time - rheobase current injected to soma causing output spikes Voltage and synaptic conductance traces are shown for all compartments. """ ############################################################################## # First, we import all necessary modules to simulate, analyze and plot this # example. import matplotlib.pyplot as plt import nest nest.ResetKernel() ############################################################################## # We then extract the receptor types and the list of recordable quantities # from the neuron model. Receptor types and recordable quantities uniquely # define the receptor type and the compartment while establishing synaptic # connections or assigning multimeters. syns = nest.GetDefaults("iaf_cond_alpha_mc")["receptor_types"] print(f"iaf_cond_alpha_mc receptor_types: {syns}") rqs = nest.GetDefaults("iaf_cond_alpha_mc")["recordables"] print(f"iaf_cond_alpha_mc recordables : {rqs}") ############################################################################### # The simulation parameters are assigned to variables. params = { "V_th": -60.0, # threshold potential "V_reset": -65.0, # reset potential "t_ref": 10.0, # refractory period "g_sp": 5.0, # somato-proximal coupling conductance "soma": {"g_L": 12.0}, # somatic leak conductance # proximal excitatory and inhibitory synaptic time constants "proximal": {"tau_syn_ex": 1.0, "tau_syn_in": 5.0}, "distal": {"C_m": 90.0}, # distal capacitance } ############################################################################### # The nodes are created using ``Create``. We store the returned handles # in variables for later reference. n = nest.Create("iaf_cond_alpha_mc", params=params) ############################################################################### # A ``multimeter`` is created and connected to the neurons. The parameters # specified for the multimeter include the list of quantities that should be # recorded and the time interval at which quantities are measured. mm = nest.Create("multimeter", params={"record_from": rqs, "interval": 0.1}) nest.Connect(mm, n) ############################################################################### # We create one current generator per compartment and configure a stimulus # regime that drives distal, proximal and soma dendrites, in that order. # Configuration of the current generator includes the definition of the start # and stop times and the amplitude of the injected current. cgs = nest.Create("dc_generator", 3) cgs[0].set(start=250.0, stop=300.0, amplitude=50.0) # soma cgs[1].set(start=150.0, stop=200.0, amplitude=-50.0) # proxim. cgs[2].set(start=50.0, stop=100.0, amplitude=100.0) # distal ############################################################################### # Generators are then connected to the correct compartments. Specification of # the ``receptor_type`` uniquely defines the target compartment and receptor. nest.Connect(cgs[0], n, syn_spec={"receptor_type": syns["soma_curr"]}) nest.Connect(cgs[1], n, syn_spec={"receptor_type": syns["proximal_curr"]}) nest.Connect(cgs[2], n, syn_spec={"receptor_type": syns["distal_curr"]}) ############################################################################### # We create one excitatory and one inhibitory spike generator per compartment # and configure a regime that drives distal, proximal and soma dendrites, in # that order, alternating the excitatory and inhibitory spike generators. sgs = nest.Create("spike_generator", 6) sgs[0].spike_times = [600.0, 620.0] # soma excitatory sgs[1].spike_times = [610.0, 630.0] # soma inhibitory sgs[2].spike_times = [500.0, 520.0] # proximal excitatory sgs[3].spike_times = [510.0, 530.0] # proximal inhibitory sgs[4].spike_times = [400.0, 420.0] # distal excitatory sgs[5].spike_times = [410.0, 430.0] # distal inhibitory ############################################################################### # Connect generators to correct compartments in the same way as in case of # current generator nest.Connect(sgs[0], n, syn_spec={"receptor_type": syns["soma_exc"]}) nest.Connect(sgs[1], n, syn_spec={"receptor_type": syns["soma_inh"]}) nest.Connect(sgs[2], n, syn_spec={"receptor_type": syns["proximal_exc"]}) nest.Connect(sgs[3], n, syn_spec={"receptor_type": syns["proximal_inh"]}) nest.Connect(sgs[4], n, syn_spec={"receptor_type": syns["distal_exc"]}) nest.Connect(sgs[5], n, syn_spec={"receptor_type": syns["distal_inh"]}) ############################################################################### # Run the simulation for 700 ms. nest.Simulate(700) ############################################################################### # Now we set the intrinsic current of soma to 150 pA to make the neuron spike. n.soma = {"I_e": 150.0} ############################################################################### # We simulate the network for another 300 ms and retrieve recorded data from # the multimeter nest.Simulate(300) rec = mm.events ############################################################################### # We create an array with the time points when the quantities were actually # recorded t = rec["times"] ############################################################################### # We plot the time traces of the membrane potential and the state of each # membrane potential for soma, proximal, and distal dendrites (`V_m.s`, `V_m.p` # and `V_m.d`). plt.figure() plt.subplot(211) plt.plot(t, rec["V_m.s"], t, rec["V_m.p"], t, rec["V_m.d"]) plt.legend(("Soma", "Proximal dendrite", "Distal dendrite"), loc="lower right") plt.axis([0, 1000, -76, -59]) plt.ylabel("Membrane potential [mV]") plt.title("Responses of iaf_cond_alpha_mc neuron") ############################################################################### # Finally, we plot the time traces of the synaptic conductance measured in # each compartment. plt.subplot(212) plt.plot(t, rec["g_ex.s"], "b-", t, rec["g_ex.p"], "g-", t, rec["g_ex.d"], "r-") plt.plot(t, rec["g_in.s"], "b--", t, rec["g_in.p"], "g--", t, rec["g_in.d"], "r--") plt.legend(("g_ex.s", "g_ex.p", "g_in.d", "g_in.s", "g_in.p", "g_in.d")) plt.axis([350, 700, 0, 1.15]) plt.xlabel("Time [ms]") plt.ylabel("Synaptic conductance [nS]") plt.show()