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run_model.py
executable file
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run_model.py
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#!/usr/bin/env python
import sys
import itertools as it
import numpy as np
import pylab as p
from neuron import h
h.load_file('stdlib.hoc')
import synapses as syn
def run_synaptic_activation():
n = CellA(El=-70., Rm=10e3, dend_scaling=0.5, with_synapses=True)
rate = 2
tend = 1000
c = 0.5
spike_times_c = syn.generate_poisson_spike_times(rate*c, tend*1e-3)[0]
presynaptic_spike_times = []
for synapse in n.synapses:
if np.random.uniform() < 0.: # correlated
spike_times_i = syn.generate_poisson_spike_times(rate*(1-c), tend*1e-3)[0]
spike_times = np.sort(np.append(spike_times_i,spike_times_c))
else: # uncorrelated
spike_times = syn.generate_poisson_spike_times(rate, tend*1e-3)[0]
synapse.set_presynaptic_spike_times(spike_times*1e3)
presynaptic_spike_times.append(spike_times)
h.topology()
rec = make_recorders(n)
for lbl in 'gampa','iampa':
rec[lbl] = h.Vector()
rec['gampa'].record(n.synapses[0].syn._ref_g)
#rec['gnmda'].record(n.synapses[0].syn._ref_gnmda)
rec['iampa'].record(n.synapses[0].syn._ref_i)
#rec['inmda'].record(n.synapses[0].syn._ref_inmda)
nu,edges,count = psth(presynaptic_spike_times,0.05,[0,tend*1e-3])
run_model(tend)
#ax = p.subplot(3,1,1)
hndl = p.figure()
p.plot(rec['t'],rec['vsoma'],'k',label='Soma')
p.plot(1e3*(edges[:-1]+edges[1:])/2,nu*10,'m',label='Rate*10')
#p.plot([0,tend],[rate,rate],'m')
#p.plot([0,tend],[np.mean(nu)*10,np.mean(nu)*10],'b')
p.plot(rec['t'],rec['vapical'],'r',label='Apical')
p.plot(rec['t'],rec['vbasal'],'g',label='Basal')
p.ylabel('Membrane voltage (mV)')
p.legend(loc='best')
#p.subplot(3,1,2,sharex=ax)
#p.plot(rec['t'],np.array(rec['gampa'])*1e3,'k',label='AMPA')
#p.plot(rec['t'],np.array(rec['gnmda'])*1e3,'r',label='NMDA')
#p.legend(loc='best')
#p.ylabel('Conductance (nS)')
#p.subplot(3,1,3,sharex=ax)
#p.plot(rec['t'],np.array(rec['iampa']),'k',label='AMPA')
#p.plot(rec['t'],np.array(rec['inmda']),'r',label='NMDA')
#p.legend(loc='best')
p.xlabel('Time (ms)')
#p.ylabel('Current (nA)')
p.savefig('uncorrelated.pdf')
p.show()
def run_simple_synaptic_activation():
n = CellA(El=-70., Rm=10e3, dend_scaling=0.5, with_synapses=True)
for i,synapse in enumerate(n.synapses):
synapse.set_presynaptic_spike_times([500+i*50])
rec = make_recorders(n)
for lbl in 'gampa','iampa':
rec[lbl] = h.Vector()
rec['gampa'].record(n.synapses[0].syn._ref_g)
#rec['gnmda'].record(n.synapses[0].syn._ref_gnmda)
rec['iampa'].record(n.synapses[0].syn._ref_i)
#rec['inmda'].record(n.synapses[0].syn._ref_inmda)
tend = i*50+1000
run_model(tend)
#ax = p.subplot(3,1,1)
p.plot(rec['t'],rec['vsoma'],'k',label='Soma')
p.plot(rec['t'],rec['vapical'],'r',label='Apical')
p.plot(rec['t'],rec['vbasal'],'g',label='Basal')
p.ylabel('Voltage (mV)')
p.legend(loc='best')
#p.subplot(3,1,2,sharex=ax)
#p.plot(rec['t'],np.array(rec['gampa'])*1e3,'k',label='AMPA')
#p.plot(rec['t'],np.array(rec['gnmda'])*1e3,'r',label='NMDA')
#p.legend(loc='best')
#p.ylabel('Conductance (nS)')
#p.subplot(3,1,3,sharex=ax)
#p.plot(rec['t'],np.array(rec['iampa']),'k',label='AMPA')
#p.plot(rec['t'],np.array(rec['inmda']),'r',label='NMDA')
#p.legend(loc='best')
p.xlabel('Time (ms)')
#p.ylabel('Current (nA)')
p.show()
def simple():
soma = h.Section()
soma.insert('pas')
soma.e_pas = -65
#soma.g_pas = 1./200e3
synapse = syn.AMPANMDASynapse(soma, 0.5, 0, 10000)
synapse.set_presynaptic_spike_times([100])
h.nmdafactor_AmpaNmda = 0
rec = {}
for lbl in 't','vsoma','vapical','vbasal','gampa','gnmda','iampa','inmda':
rec[lbl] = h.Vector()
rec['t'].record(h._ref_t)
rec['vsoma'].record(soma(0.5)._ref_v)
rec['gampa'].record(synapse.syn._ref_gampa)
rec['gnmda'].record(synapse.syn._ref_gnmda)
rec['iampa'].record(synapse.syn._ref_iampa)
rec['inmda'].record(synapse.syn._ref_inmda)
h.load_file('stdrun.hoc')
h.celsius = 35
h.cvode_active(1)
h.cvode.maxstep(10)
h.tstop = 500
h.finitialize(soma.e_pas)
h.run()
p.subplot(3,1,1)
p.plot(rec['t'],rec['vsoma'],'k',label='Soma')
p.subplot(3,1,2)
p.plot(rec['t'],np.array(rec['gampa'])*1e3,'k',label='AMPA')
p.plot(rec['t'],np.array(rec['gnmda'])*1e3,'r',label='NMDA')
p.legend(loc='best')
p.ylabel('Conductance (nS)')
p.subplot(3,1,3)
p.plot(rec['t'],np.array(rec['iampa']),'k',label='AMPA')
p.plot(rec['t'],np.array(rec['inmda']),'r',label='NMDA')
p.legend(loc='best')
p.xlabel('Time (ms)')
p.ylabel('Current (nA)')
p.show()
if __name__ == '__main__':
#simple()
run_step()
#run_simple_synaptic_activation()
#run_synaptic_activation()