Source code for compartment_net

# ---
# Filename:
# Description:
# Author:Subhasis Ray
# Maintainer:
# Created: Sat Aug 11 14:30:21 2012 (+0530)
# Version:
# Last-Updated: Sun Aug 12 15:45:38 2012 (+0530)
#           By: subha
#     Update #: 521
# URL:
# Keywords:
# Compatibility:

# Commentary:
# A demo to create a network of single compartmental neurons connected
# via alpha synapses.

# Change log:
# This program 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 3, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING.  If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.

# Code:

import sys
import os
from pylab import *
import numpy as np
import matplotlib.pyplot as plt

import moose
from ionchannel import create_1comp_neuron

[docs]def create_population(container, size): """Create a population of `size` single compartmental neurons with Na+ and K+ channels. Also create SpikeGen objects and SynChan objects connected to these which can act as plug points for setting up synapses later. This uses **ionchannel.create_1comp_neuron**. """ path = container.path print((path, size, type(path))) comps = create_1comp_neuron('{}/neuron'.format(path), number=size) synpath = path+'/synchan' print((synpath, size, type(size))) synchan = moose.vec(synpath, n=size, dtype='SynChan') synchan.Gbar = 1e-8 synchan.tau1 = 2e-3 synchan.tau2 = 2e-3 m = moose.connect(comps, 'channel', synchan, 'channel', 'OneToOne') synhandler = moose.vec('{}/synhandler'.format(path), n=size, dtype='SimpleSynHandler') moose.connect(synhandler, 'activationOut', synchan, 'activation', 'OneToOne') spikegen = moose.vec('{}/spikegen'.format(path), n=size, dtype='SpikeGen') spikegen.threshold = 0.0 m = moose.connect(comps, 'VmOut', spikegen, 'Vm', 'OneToOne') return {'compartment': comps, 'spikegen': spikegen, 'synchan': synchan, 'synhandler': synhandler}
[docs]def make_synapses(spikegen, synhandler, connprob=1.0, delay=5e-3): """ Create synapses from spikegen array to synchan array. Parameters ---------- spikegen: vec of SpikGen elements Spike generators from neurons. synhandler: vec of SynHandler elements Handles presynaptic spike event inputs to synchans. connprob: float in range (0, 1] connection probability between any two neurons delay: float (mean delay of synaptic transmission) Individual delays are normally distributed with sd=0.1*mean. """ for sh in synhandler: scount = len(spikegen) sh.synapse.num = scount sh.synapse.vec.delay = 5e-3 for ii, syn in enumerate(sh.synapse): msg = moose.connect(spikegen[ii], 'spikeOut', syn, 'addSpike') print(('Connected', spikegen[ii].path, 'to', syn.path, 'on', sh.path))
[docs]def create_network(size=2): """ Create a network containing two neuronal populations, pop_A and pop_B and connect them up. """ net = moose.Neutral('network') pop_a = create_population(moose.Neutral('/network/pop_A'), size) print(pop_a) pop_b = create_population(moose.Neutral('/network/pop_B'), size) make_synapses(pop_a['spikegen'], pop_b['synhandler']) pulse = moose.PulseGen('pulse') pulse.level[0] = 1e-9 pulse.delay[0] = 0.02 # disable the pulsegen pulse.width[0] = 40e-3 pulse.delay[1] = 1e9 data = moose.Neutral('/data') vm_a = moose.Table('/data/Vm_A', n=size) moose.connect(pulse, 'output', pop_a['compartment'], 'injectMsg', 'OneToAll') moose.connect(vm_a, 'requestOut', pop_a['compartment'], 'getVm', 'OneToOne') vm_b = moose.Table('/data/Vm_B', size) moose.connect(vm_b, 'requestOut', pop_b['compartment'], 'getVm', 'OneToOne') gksyn_b = moose.Table('/data/Gk_syn_b', n=size) moose.connect(gksyn_b, 'requestOut', pop_b['synchan'], 'getGk', 'OneToOne') pulsetable = moose.Table('/data/pulse') pulsetable.connect('requestOut', pulse, 'getOutputValue') return {'A': pop_a, 'B': pop_b, 'Vm_A': vm_a, 'Vm_B': vm_b, 'Gsyn_B': gksyn_b }
[docs]def main(): """ This example illustrates how to create a network of single compartmental neurons connected via alpha synapses. It also shows the use of SynChan class to create a network of single-compartment neurons connected by synapse. """ simtime = 0.1 simdt = 0.25e-5 plotdt = 0.25e-3 netinfo = create_network(size=2) vm_a = netinfo['Vm_A'] vm_b = netinfo['Vm_B'] gksyn_b = netinfo['Gsyn_B'] for ii in range(10): moose.setClock(ii, simdt) moose.setClock(18, plotdt) moose.reinit() moose.start(simtime) plt.subplot(221) for oid in vm_a.vec: print((oid, oid.vector.shape)) plt.plot(oid.vector, label=oid.path) plt.legend() plt.subplot(223) for oid in vm_b.vec: plt.plot(oid.vector, label=oid.path) plt.legend() plt.subplot(224) for oid in gksyn_b.vec: plt.plot(oid.vector, label=oid.path) plt.legend()
# # ends here