def runSimulation(young_chem1_con1, young_elec1_con1, numNeurons, fileIndex, simTime, inhibInd, age, delay, stimInterval, con):
"""
This defintion will run the simulation.
"""
print 'Run simulation!!'
# Load file to make model regular spiking excitory cells.
h.load_file ("sPY_template")
# Load file to make model fast spiking inhibitory cells.
h.load_file ("sIN_template")
#Create neurons in the networks.
numSegs = 3
Neurons = []
for i in range(0, numNeurons):
if i < inhibInd:
neuron = h.sPY()
Neurons.append(neuron)
else:
neuron = h.sIN()
Neurons.append(neuron)
#Import connectivity between neurons.
gapsYoung = constructConnections1( young_elec1_con1, numNeurons, Neurons)
synYoung,ncYoung = constructConnections2( young_chem1_con1, numNeurons, Neurons, inhibInd, delay )
#Create vectors for recording potentials, currents, etc in the neural network during the simulation.
vec = recordData( numNeurons, Neurons )
#Stimulate system with random noise.
stims = []
for i in range(0,numNeurons):
stims.append(makeStimulus(Neurons[i],simTime,int(stimInterval)))
#Run the simulation.
h.load_file("stdrun.hoc")
h.init()
h.tstop = simTime
h.run()
#Write results.
file = open(path + 'spikes' + str(con) + '_' + str(age) + '_' + str(fileIndex) + '_' + str(stimInterval) + '.txt',"wb")
print "Writing results to..."
print file
for j in range(0,numNeurons):
canFire = 1
var = str(j+1)
for i in range(0,len(vec ['t '])):
if (vec[var][i] > 0) & (canFire == 1):
file.write(str(j+1)+"\n")
file.write(str(vec['t '][i])+"\n")
canFire = 0
if (vec[var][i] < 0):
canFire = 1
file.close()
def runSimulation(young_chem1_con1, young_elec1_con1, numNeurons, fileIndex, simTime, inhibInd, age, delay, stimInterval, con, strength):
"""
This defintion will run the simulation.
"""
print 'Run simulation!!'
# Load file to make model regular spiking excitory cells.
h.load_file ("sPY_template")
# Load file to make model fast spiking inhibitory cells.
h.load_file ("sIN_template")
#Create neurons in the networks.
numSegs = 3
Neurons = []
for i in range(0, numNeurons):
if i < inhibInd:
neuron = h.sPY()
Neurons.append(neuron)
else:
neuron = h.sIN()
Neurons.append(neuron)
#Import connectivity between neurons.
#gapsYoung = constructConnections1( young_elec1_con1, numNeurons, Neurons)
synYoung,ncYoung = constructConnections2( young_chem1_con1, numNeurons, Neurons, inhibInd, delay, strength )
#Create vectors for recording potentials, currents, etc in the neural network during the simulation.
vec = recordData( numNeurons, Neurons )
#Stimulate system with random noise.
stims = []
for i in range(0,numNeurons):
stims.append(makeStimulus(Neurons[i],simTime,int(stimInterval)))
#Run the simulation.
h.load_file("stdrun.hoc")
h.init()
h.tstop = simTime
h.run()
h.load_file("sPY.tem")
#h.xopen("sPY.tem") # read geometry file
PY = [] # create PY cells
PYVtrace = []
for i in range(ncorticalcells):
cell = h.sPY()
#cell.soma.v = randvolt.repick()
PY.append(cell)
h.load_file("sIN.tem")
#h.xopen("sIN.tem") # read geometry file
IN = [] # create IN cells
INVtrace = []
for i in range(ncorticalcells):
cell = h.sIN()
#cell.soma.v = randvolt.repick()
IN.append(cell)
h.load_file("TC.tem")
#h.xopen("TC.tem")
TC = [] # create TC cells
TCVtrace = []
for i in range(nthalamiccells):
cell = h.sTC()
#cell.soma.v = randvolt.repick()
TC.append(cell)
h.load_file("RE.tem")
#h.xopen("RE.tem")
RE = [] # create RE cells
from itertools import chain
from neuron import h
import random
import os
import time
import sys
import numpy
from pylab import *
# Try to make the excitory cell.
h.load_file("sPY_template")
exc = h.sPY()
# Try to make the excitory cell.
h.load_file("sIN_template")
inh = h.sIN()
# Stimulate the exc cell.
stimNc = h.NetStim()
stimNc.noise = 1
stimNc.start = 150
stimNc.number = 1
stimNc.interval = 1
syn = h.ExpSyn(0.5, sec=exc.soma[0])
nc = h.NetCon(stimNc, syn)
nc.weight[0] = 5
nc.record()
"""
# Stimulate the exc cell.
stimNcI = h.NetStim()
stimNcI.noise = 1