def run(netdef,tosave,modify,procs,thisProc,stims,param,repeats,sim_time,SaveSpikes,SaveVoltage,SaveConductance,SaveCurrent):
net = netdef()
if SaveVoltage:
net.recordVoltage()
repeats = int(repeats)
# Randomseed was 200 for most figures
# Changed to 200 for rat
# Changed to 200 for anurans
s = Simulation(net, randomseed=202,delay=25)
s.verbose = False
s.sim_time = sim_time
s.dt = 0.050
total = len(stims)*len(param)*repeats
spp = ceil(float(total)/procs)
start = thisProc*spp
end = (thisProc+1)*spp
count = 0
for a in param:
s.set_amplitude(net.sim_amp)
for d in stims*repeats:
if count >= start and count < end:
net = modify(net,a,d)
progress.update(count-start,spp,thisProc)
s.stim_dur = d
s.run()
key = [a,d]
net.savecells(tosave, key, spikes=SaveSpikes,voltage=SaveVoltage,conductance=SaveConductance,current=SaveCurrent)
count += 1
progress.update(spp,spp,thisProc)
r = [thisProc,net.savedparams,net.savedcells]
return r
import network
import neuron
import netshow as ns
import progress
# Create our network
networks = {}
networks["C"] = network.DTN_Coincidence()
networks["AC"] = network.DTN_AntiCoincidence()
# Initialize the simulation with the network
s = {}
for net in networks:
s[net] = Simulation(networks[net])
s[net].verbose = False
s[net].sim_time = 100
s[net].dt = 0.025
# Run the simulations
stims = [i for i in range(1,31,1)]
repeats = 15
param = [(i*2)+10 for i in range(11)]
total = len(stims)*len(param)*repeats
print "Running %d simulations..." % total
count = 0
for a in param:
for net in networks:
