#!/usr/bin/env python
"""script for feedforward circuit from Figure 7"""
# _title_ : FF_circuit.py
# _author_ : Katharina Anna Wilmes
# _mail_ : katharina.anna.wilmes __at__ cms.hu-berlin.de
# --Imports--
import sys
import os
import math
from time import time
import matplotlib.pyplot as plt
import numpy as np
import neuron
from neuron import h
from neuronmodel import *
from config_circuit import *
from sim import *
#set path for saving data
path = './'
NO_EPSPs = 8
NO_REPS = 1
DT=0.1 # ms, set the integration step
POST_AMP = 0.3 # nA, amplitude of current injection to trigger the AP/bAP
WARM_UP=1000 # ms, until steady state
identifier = '2015-01-13-00h00m00s'
savepath = '%s%s'%(path,identifier)
if not os.path.exists(savepath):
os.mkdir(savepath)
def get_interneuron(inh_type=0):
interneuron = neuron.h.Section()
interneuron.L = 67
interneuron.diam = 67
interneuron.nseg = 1
interneuron.Ra = 100
interneuron.cm = 1
interneuron.insert('pas')
for seg in interneuron:
seg.pas.g = 0.00015
seg.pas.e = -70
# fast interneuron
if inh_type == 0:
interneuron.insert('hh2')
interneuron.vtraub_hh2 = -55 #resting Vm, BJ was -55
interneuron.gnabar_hh2 = 0.05 #McCormick=15 muS, thal was 0.09
interneuron.gkbar_hh2 = 0.01 #spike duration of interneurons
interneuron.ena = 50
interneuron.ek = -100
# slower interneuron
elif inh_type == 1:
interneuron.insert('hh3')
interneuron.vtraub_hh3 = -55 #resting Vm, BJ was -55
interneuron.gnabar_hh3 = 0.05 #McCormick=15 muS, thal was 0.09
interneuron.gkbar_hh3 = 0.01 #spike duration of interneurons
interneuron.ena = 50
interneuron.ek = -100
else:
raise ValueError
return interneuron
def main():
my_rawdata = {}
#inputs
freq = params['Input']['freq'].value
# Synapses
pos = params['Synapse']['pos'].value
w_ei = params['Input']['w_ei'].value
w_ii = params['Input']['w_ii'].value
w_ee = params['Input']['w_ee'].value
EPSP_pos = params['Synapse']['EPSP_pos'].value
inh_delay = params['shunt']['shunt_delay'].value
inh_pos = params['shunt']['inh_pos'].value
inh_type = params['shunt']['inh_type'].value
interneuron_target = params['shunt']['interneuron_target'].value
sim_params = params['sim']
for w_ie in [0.0,0.05]:
cell = Neuron()
sim = Simulation(cell,sim_params)
sim.dt = DT
sim.v_init = -70
interneuron = get_interneuron(inh_type)
# excitatory input neuron
ns = h.NetStim()
ns.number = NO_REPS # (average) number of spikes
ns.interval = 1000.0/freq
ns.start = WARM_UP # ms (most likely) start time of first spike
# excitatory to inhibitory synapse
ex_inh = neuron.h.Exp2Syn(interneuron(0.5))
ex_inh.tau1 = 0.5 # ms rise time
ex_inh.tau2 = 2 # ms decay time
ex_inh.e = 0 # mV reversal p
# excite interneuron
nc_ex_inh = h.NetCon(ns,ex_inh,1,0,w_ei)
# VIP interneuron
ns_VIP = h.NetStim()
ns_VIP.number = NO_REPS # (average) number of spikes
ns_VIP.interval = 1000.0/freq
ns_VIP.start = WARM_UP # ms (most likely) start time of first spike
#inhibitory to inhibitory synapse
inh_inh = neuron.h.Exp2Syn(interneuron(0.5))
inh_inh.tau1 = 0.5 # ms rise time
inh_inh.tau2 = 5 # ms decay time
inh_inh.e = -73 # mV reversal p
w_ii = float(params['Input']['w_ii'].value)
nc_inh_inh = h.NetCon(ns_VIP,inh_inh,1,0,w_ii)
# excitatory to excitatory synapse
ex_ex = h.List()
nc_ex = h.List()
branch = 0
for i in range(1,NO_EPSPs+1):
pos = (i) * 0.1
ex = neuron.h.Exp2Syn(cell.apical(pos))
w = w_ee/NO_EPSPs #0.016
ex.tau1 = 0.5 # ms rise time
ex.tau2 = 2 # ms decay time
ex.e = 0 # mV reversal potential
# excite postsynaptic cell
nc = h.NetCon(ns,ex,1,0,w)
ex_ex.append(ex)
nc_ex.append(nc)
#inhibitory to excitatory synapse
if interneuron_target == 0:
inh_ex = neuron.h.Exp2Syn(cell.apical_prox(inh_pos))
elif interneuron_target == 1:
inh_ex = neuron.h.Exp2Syn(cell.apical(inh_pos))
inh_ex.tau1 = 0.5 # ms rise time
inh_ex.tau2 = 5 # ms decay time
inh_ex.e = -73 # mV reversal p
# connect interneuron to postsynaptic cell
nc_inh_ex = h.NetCon(interneuron(0.5)._ref_v, inh_ex,1,inh_delay,w_ie, sec = interneuron)
nc_vec = h.Vector()
nc_inh_ex.record(nc_vec)
total_time = WARM_UP+NO_REPS*(1000.0/freq)+100
sim.sim_time = total_time
sim.interneuron = interneuron
# recording
trec = h.Vector()
trec.record(h._ref_t)
vrec = h.Vector()
vrec.record(cell.soma(0.5)._ref_v)
vdrec = h.Vector()
vdrec.record(cell.branches[0](0.5)._ref_v)
vorec = h.Vector()
vorec.record(cell.oblique_branch(0.9)._ref_v)
vbrec = h.Vector()
vbrec.record(cell.basal_main(0.9)._ref_v)
vprox = h.Vector()
vprox.record(cell.apical_prox(0.9)._ref_v)
vinhrec = h.Vector()
vinhrec.record(interneuron(0.5)._ref_v)
cadrec = h.Vector()
cadrec.record(cell.branches[0](0.5)._ref_ica)
# run simulation
sim.go()
t = np.array(trec)
v = np.array(vrec)
vd = np.array(vdrec)
vo = np.array(vorec)
vb = np.array(vbrec)
vinh = np.array(vinhrec)
vprox = np.array(vprox)
nc_v = np.array(nc_vec)
cad = np.array(cadrec)
plt.figure()
plt.plot(t,v,'r',label='v')
plt.hold(True)
plt.plot(t,vo,'g',label='vo')
if w_ie == 0.05:
plt.plot(nc_v[0]+inh_delay, -75, '|',color = '#FF8300')
plt.xlabel("Time [ms]", fontsize = 'large')
plt.ylabel("Voltage [mV]", fontsize = 'large')
plt.xlim(1000,1010)
plt.legend(('soma','dendrite'))
plt.savefig('%s/vrec_wee%d_type%s_target%s.eps'%(savepath, w_ie*1000,inh_type,interneuron_target))
my_rawdata['t'] = t
my_rawdata['v'] = v
my_rawdata['vd'] = vd
my_rawdata['vo'] = vo
my_rawdata['vb'] = vb
my_rawdata['vinh'] = vinh
my_rawdata['cad'] = cad
rawdata = {'raw_data': my_rawdata}
return rawdata
if __name__ == '__main__':
rawdata = main()
