# -*- 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 nest import matplotlib.pyplot as plt 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.} ############################################################################### # 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()