def IntFire1(cell_prop):
"""Loads a point integrate and fire neuron"""
model_params = cell_prop.model_params
hobj = h.IntFire1()
hobj.tau = model_params['tau'] * 1000.0 # Convert from seconds to ms.
hobj.refrac = model_params['refrac'] * 1000.0 # Convert from seconds to ms.
return hobj
def IntFire1(cell, template_name, dynamics_params):
"""Loads a point integrate and fire neuron"""
hobj = h.IntFire1()
hobj.tau = dynamics_params['tau'] * 1000.0 # Convert from seconds to ms.
hobj.refrac = dynamics_params[
'refrac'] * 1000.0 # Convert from seconds to ms.
return hobj
def IntFire1(cell_prop):
"""Set parameters for the IntFire1 cell models."""
params_file = cell_prop['params_file']
with open(params_file) as params_file:
params = json.load(params_file)
hobj = h.IntFire1()
hobj.tau = params['tau'] * 1000.0 # Convert from seconds to ms.
hobj.refrac = params['refrac'] * 1000.0 # Convert from seconds to ms.
return hobj
def __init__(self):
self.cell = h.IntFire1()
self.cell.m = 0.
self.recorder = h.NetCon(self.cell, None)
self.spk = h.Vector()
self.recorder.record(self.spk)
#Store temporary data in
soma, axon, bd, ad = constructGeometry(x)
Soma.append(soma)
Axon.append(axon)
BD.append(bd)
AD.append(ad)
#Set biophysics for the different neural structures. IAF will contain all data pertaining to the refractory period between spikes.
IAF = []
counter = 0
for i in range(0, numNeurons):
for sec in Axon[i]:
sec.insert('hh')
sec.nseg = numSegs
IAF.append(h.IntFire1(0.5))
IAF[counter].tau = tau
IAF[counter].refrac = refrac
for seg in sec:
seg.hh.gnabar = 0.25
seg.hh.gl = .0001666
seg.hh.el = -60.0
counter = counter + 1
for sec in BD[i]:
sec.insert('pas')
sec.nseg = numSegs
IAF.append(h.IntFire1(0.5))
IAF[counter].tau = tau
IAF[counter].refrac = refrac
counter = counter + 1
for seg in sec:
from neuron import h, gui
h.tstop = 100
h.load_file('netparmpi.hoc')
ncell = 128
pnm = h.ParallelNetManager(ncell)
pc = pnm.pc
# try with multiple threads
pnm.pc.nthread(32)
pnm.round_robin()
for i in range(ncell):
if pnm.gid_exists(i):
pnm.register_cell(i, h.IntFire1())
for i in range(ncell):
pnm.nc_append(i, (i + 1) % ncell, -1, 1.1, 2)
# stimulate
if pnm.gid_exists(4):
stim = h.NetStim(.5)
ncstim = h.NetCon(stim, pnm.pc.gid2obj(4))
ncstim.weight[0] = 1.1
ncstim.delay = 1
stim.number = 1
stim.start = 1
pnm.set_maxstep(100) # will end up being 2
tempSectionName = "soma" + connections[i][9] print tempSectionName print soma8.name syn.append(h.ExpSyn(float(connections[i][8]), sec = tempSectionName)) """ syn1 = h.ExpSyn(0.5, sec=soma1) syn2 = h.ExpSyn(0.5, sec=soma2) syn3 = h.ExpSyn(0.5, sec=soma3) syn4 = h.ExpSyn(0.5, sec=soma4) syn5 = h.ExpSyn(0.5, sec=soma5) syn6 = h.ExpSyn(0.5, sec=soma6) syn7 = h.ExpSyn(0.5, sec=soma7) syn8 = h.ExpSyn(0.5, sec=soma8) syn9 = h.ExpSyn(0.5, sec=soma5) IAF = h.IntFire1(0.5, sec=soma1) IAF.tau = 10 IAF.refrac = 5 # connect the pre - synaptic section to the # synapse object soma2.push() nc1 = h.NetCon(soma2(0.5)._ref_v, syn1) nc1.weight[0] = 1.0 soma3.push() nc2 = h.NetCon(soma3(0.5)._ref_v, syn2) nc2.weight[0] = 1.0 soma4.push() nc3 = h.NetCon(soma4(0.5)._ref_v, syn3) nc3.weight[0] = 1.0
def runSimulation(numSegs, tau, refrac, simTime, numNeurons, path, current):
"""
This defintion will analyze a specific random network geometry and random connectivity type.
"""
print 'Run simulation!!'
#Create lists for somas, axons, basal dendrites, and apical dendrites for each neuron in the network.
Soma = []
Axon = []
Dend = []
#Begin creating neurons individually from the various geometrical files.
for index in range(0, numNeurons):
soma, axon, dend = constructGeometry(index, dendrites, connections,
numNeurons)
Soma.append(soma)
Axon.append(axon)
Dend.append(dend)
#Set biophysics for the different neural structures. IAF will contain all data pertaining to the refractory period between spikes.
IAF = []
counter = 0
for i in range(0, numNeurons):
for sec in Dend[i]:
sec.insert('pas')
sec.nseg = numSegs
IAF.append(h.IntFire1(0.5))
IAF[counter].tau = tau
IAF[counter].refrac = refrac
counter = counter + 1
for seg in sec:
seg.pas.g = 0.0002
seg.pas.e = -65
for sec in Soma:
sec.insert('hh')
sec.nseg = numSegs
IAF.append(h.IntFire1(0.5))
IAF[counter].tau = tau
IAF[counter].refrac = refrac
for seg in sec:
seg.hh.gnabar = 0.25
seg.hh.gl = .0001666
seg.hh.el = -60.0
counter = counter + 1
for sec in Axon:
sec.insert('hh')
sec.nseg = numSegs
IAF.append(h.IntFire1(0.5))
IAF[counter].tau = tau
IAF[counter].refrac = refrac
for seg in sec:
seg.hh.gnabar = 0.25
seg.hh.gl = .0001666
seg.hh.el = -60.0
counter = counter + 1
#Import connectivity between neurons.
synapses, networkConnections = constructConnections(
connections, dendrites, numNeurons, Soma, Axon, Dend)
#Create vectors for recording potentials, currents, etc in the neural network during the simulation.
vec = recordData(numNeurons, Soma)
#Stimulate system with random noise.
stims = []
for i in range(0, numNeurons):
#stim.append(h.IClamp(Soma[i](0.5) ))
#stim[i].delay = 5
#stim[i].dur = 100
#stim[i].amp = 10
stims.append(makeStimulus(Soma[i], simTime))
# run the simulation
h.load_file("stdrun.hoc")
h.init()
h.tstop = simTime
h.run()
#Save spiking data for visual representation.
file = open(path + 'spikes' + str(current) + '.txt', "wb")
print path + 'spikes' + str(current) + '.txt'
for i in range(0, len(vec['t '])):
for j in range(0, numNeurons + 1):
for var in vec:
if (var == str(j + 1)):
file.write(str(vec[var][i]) + "\n")
elif (j == numNeurons) & (var == 't '):
file.write(str(vec['t '][i]) + "\n")
file.close()
#Destroy neuron sections.
Dend = []
Axon = []
Soma = []
