def main(args=None):
"""Main"""
# Simulation general parameters
data.dt = 0.1
lb.h.dt = data.dt
lb.h.steps_per_ms = 1.0 / lb.h.dt
data.st_onset = 200.0
data.st_duration = 200.
data.TSTOP = 600
data.TRIALS = 5
data.Egmax = 1
data.Igmax = 1
data.Irev = -80
data.Ensyn = 100
data.Insyn = int(data.Ensyn * 0.2)
data.bEnsyn = 200
data.bInsyn = int(data.bEnsyn * 0.2)
# Simulation CONTROL
data.model = 'L23'
data.locType = 'fixed'
data.simType = 'rateIteration'
data.fixedINPUT = False
data.ACTIVE = True
data.ACTIVEdend = True
data.ACTIVEdendNa = True
data.ACTIVEdendCa = True
data.ACTIVEaxonSoma = True
data.ACTIVEhotSpot = True
data.SYN = True
data.SPINES = False
data.ICLAMP = False
data.NMDA = True
data.GABA = True
data.BGROUND = True
global model
# Create neuron and add mechanisms
if data.model == 'BS': model = lb.BS()
if data.model == 'L23': model = lb.L23()
if data.model == 'CELL': model = lb.CELL()
if data.SPINES: lb.addSpines(model)
if data.ACTIVE:
lb.init_active(model,
axon=data.ACTIVEaxonSoma,
soma=data.ACTIVEaxonSoma,
dend=data.ACTIVEdend,
dendNa=data.ACTIVEdendNa,
dendCa=data.ACTIVEdendCa)
if data.ACTIVEhotSpot: lb.hotSpot(model)
# Generate synapse locations
if data.locType == 'random':
data.Elocs = genRandomLocs(data, model, data.Ensyn)
data.Ilocs = genRandomLocs(data, model, data.Insyn)
if data.locType == 'fixed':
loadElocs = np.load('./Elocs.npy')
data.Elocs = loadElocs[0:data.Ensyn]
loadIlocs = np.load('./Ilocs.npy')
data.Ilocs = loadIlocs[0:data.Insyn]
if data.BGROUND:
data.bElocs = genRandomLocs(data, model, data.bEnsyn)
data.bIlocs = genRandomLocs(data, model, data.bInsyn)
data.Elocs = np.vstack((data.Elocs, data.bElocs))
data.Ilocs = np.vstack((data.Ilocs, data.bIlocs))
# Hack for freezing the background
# Uncomment first 2 lines and comment out last 2 for
# random background
#data.bElocs = loadElocs[data.Ensyn+1:]
#data.bIlocs = loadIlocs[data.Insyn+1:]
data.Elocs = loadElocs
data.Ilocs = loadIlocs
# Insert synapses
if data.SYN:
lb.add_AMPAsyns(model, locs=data.Elocs, gmax=data.Egmax)
if data.NMDA: lb.add_NMDAsyns(model, locs=data.Elocs, gmax=data.Egmax)
if data.GABA:
lb.add_GABAsyns(model,
locs=data.Ilocs,
gmax=data.Igmax,
rev=data.Irev)
# Insert IClamp
data.iclampLoc = ['dend', 0.5, 28]
data.iclampOnset = 50
data.iclampDur = 250
data.iclampAmp = 0
if data.ICLAMP:
if data.iclampLoc[0] == 'soma':
lb.add_somaStim(model,
data.iclampLoc[1],
onset=data.iclampOnset,
dur=data.iclampDur,
amp=data.iclampAmp)
if data.iclampLoc[0] == 'dend':
lb.add_dendStim(model,
data.iclampLoc[1],
data.iclampLoc[2],
onset=data.iclampOnset,
dur=data.iclampDur,
amp=data.iclampAmp)
#----------------------------------------------------------------------------
# Data storage lists
data.vdata, data.vDdata, data.gdata, data.idata, data.caDdata, data.vsec = [], [], [], [], [], []
data.rates = []
#----------------------------------------------------------------------------
# Run simulation
# Specific parameters
data.rateRange = np.arange(8, 9, 10)
data.lagRange = np.arange(-100, 110, 10)
data.iRange = np.arange(-0.1, 0.2, 0.1)
data.singleRate = 51
data.tInterval = 1
data.EbGroundRate = 2
data.IbGroundRate = 2
data.recordDend = True
data.recordSec = False
if data.simType == 'rateIteration':
SIM_rateIteration(data, model, data.rateRange, data.BGROUND)
if data.simType == 'iSteps':
SIM_currentSteps(data, model, data.iRange, data.BGROUND)
#----------------------------------------------------------------------------
# Save data
modelData = sc.emptyObject()
lb.props(modelData)
tstamp = time.strftime("sim%Y%b%d_%H%M%S")
tstamp = 'temp'
dataList = [data, modelData]
fname = './' + tstamp + '.pkl'
f = open(fname, 'wb')
pickle.dump(dataList, f)
f.close()
def run_iclamp(iRange, run, nogui, vFileName):
# Data saving object
data = sc.emptyObject()
# Simulation general parameters
data.dt = 0.025
lb.h.dt = data.dt
lb.h.steps_per_ms = 1.0/lb.h.dt
data.st_onset = 200.0
data.st_duration = 200.
data.TSTOP = 400
# Simulation CONTROL
data.model = 'L23'
data.ACTIVE = True
data.ACTIVEdend = True
data.ACTIVEdendNa = True
data.ACTIVEdendCa = True
data.ACTIVEaxonSoma = True
data.ACTIVEhotSpot = False ##### Note: no hot spots!!!
data.SYN = False
data.SPINES = False
data.ICLAMP = True
data.NMDA = False
data.GABA = False
data.BGROUND = False
model = lb.L23()
print("Created cell: %s"%model.__class__)
if data.SPINES: lb.addSpines(model)
if data.ACTIVE: lb.init_active(model, axon=data.ACTIVEaxonSoma,
soma=data.ACTIVEaxonSoma, dend=data.ACTIVEdend,
dendNa=data.ACTIVEdendNa, dendCa=data.ACTIVEdendCa)
if data.ACTIVEhotSpot: lb.hotSpot(model)
#data.iclampLoc = ['dend', 0.5, 28]
data.iclampLoc = ['soma', 0.5]
data.iclampOnset = 50
data.iclampDur = 250
data.iclampAmp = 0
if data.ICLAMP:
if data.iclampLoc[0]=='soma':
lb.add_somaStim(model, data.iclampLoc[1], onset=data.iclampOnset,
dur=data.iclampDur, amp=data.iclampAmp)
if data.iclampLoc[0]=='dend':
lb.add_dendStim(model, data.iclampLoc[1], data.iclampLoc[2],
onset=data.iclampOnset, dur=data.iclampDur, amp=data.iclampAmp)
h('count=0')
h('forall {print "--------------- ", secname() \n count=count+1 } ')
h('print "Number of sections: ", count')
if run:
data.vdata, data.vDdata, data.gdata, data.idata, data.caDdata, data.vsec = [], [], [], [], [], []
data.rates = []
data.recordDend = False
data.recordSec = True
data.iRange = iRange
print("Running current clamp simulation with parameters:")
for k in sorted(data.__dict__.keys()):
print(" %s:\t\t%s"%(k, data.__dict__[k]))
SIM_currentSteps(data, model, data.iRange, data.BGROUND)
modelData = sc.emptyObject()
lb.props(modelData)
tstamp = time.strftime("sim%Y%b%d_%H%M%S")
tstamp = 'temp'
dataList = [data, modelData]
fname = './test/'+tstamp+'.pkl'
f = open(fname, 'wb')
pickle.dump(dataList, f)
f.close()
times = data.taxis
vFile = open(vFileName, "w")
for i in range(len(times)):
vFile.write('%s'%(times[i]/1000.0))
for vd in data.vdata:
vFile.write('\t%s'%(vd[i]/1000.0))
vFile.write('\n')
vFile.close()
print('Written traces to: %s'%vFileName)
if not nogui:
import matplotlib.pyplot as plt
fig = plt.figure()
for volts in data.vdata:
plt.plot(times, volts)
fig.show()
print("Done!")
return model
print(" %s:\t\t%s" % (k, data.__dict__[k]))
model.stim.amp = step
taxis, v, vD, g, i, ca, vsec = lb.simulate(model,
t_stop=data.TSTOP,
NMDA=data.NMDA,
recDend=data.recordDend)
if data.NMDA == True:
idata.append(np.sum(np.array(i).min(1)))
gdata.append(np.sum(np.array(g).max(1)))
storeSimOutput(data, v, vD, idata, gdata, r=0, ca=ca, vSec=vsec)
data.taxis = taxis
#----------------------------------------------------------------------------
# Data saving object
data = sc.emptyObject()
def main(args=None):
"""Main"""
# Simulation general parameters
data.dt = 0.1
lb.h.dt = data.dt
lb.h.steps_per_ms = 1.0 / lb.h.dt
data.st_onset = 200.0
data.st_duration = 200.
data.TSTOP = 600
data.TRIALS = 5
data.Egmax = 1
data.Igmax = 1
def save_sim(data, out_binary=False, out_vdend=False, out_pickle=False, outdir='data', rate='0', omega='0', iAMP='0'):
if not os.path.exists(outdir):
os.makedirs(outdir)
modelData = sc.emptyObject()
lb.props(modelData)
description = 'Simulation type:'+data.simType+'. Fixed patterns, distributed.'
if data.NMDA:
data.Ntype='NMDA'
else:
data.Ntype='AMPA'
if data.NMDAkinetic:
data.Ntype='NMDAk'
if data.ACTIVE:
if data.ACTIVEdend:
data.act='Adend'
else :
data.act='Asoma'
else:
data.act='passive'
if (data.stimType == 'allDend'):
filename=data.Ntype+'_ApN'+str(data.ApN)+'_'+data.locBias+'_'+data.act+'_r'+str(rate)+'_o'+str(omega)+'_g'
elif (data.stimType == 'mixedori'):
filename=data.Ntype+'_ApN'+str(data.ApN)+'_'+data.locBias+'_'+data.act+'_r'+str(rate)+'_o'+str(omega)+'_i'+str(iAMP)+'_g'
filename = filename + '_b' + str(data.inburstrate)
elif (data.stimType == 'orientations'):
filename=data.Ntype+'_ApN'+str(data.ApN)+'_'+data.locBias+'_'+data.act+'_r'+str(rate)+'_o'+str(omega)+'_i'+str(iAMP)+'_g'
elif (data.stimType == 'minis'):
filename=data.Ntype+'_ApN'+str(data.ApN)+'_'+data.locBias+'_'+data.act+'_r'+str(rate)+'_o'+str(omega)+'_i'+str(iAMP)+'_g'
else :
filename=data.Ntype+'_NAR'+str(data.NAR)+'_'+data.locBias+'_'+data.act+'_r'+str(rate)+'_o'+str(omega)+'_i'+str(iAMP)+'_g'
#filename='temp'
if out_pickle:
dataList = [data, modelData]
fname = './'+outdir+'/'+filename+'.pkl'
f = open(fname, 'wb')
pickle.dump(dataList, f)
f.close()
if out_binary:
#---------------------------------------------
# WRITE the response in a binary file to read it with R
mat = np.array(data.vdata)
bfname = './'+outdir+'/vdata_'+filename+'.bin'
print bfname
# create a binary file
binfile = file(bfname, 'wb')
# and write out two integers with the row and column dimension
header = struct.pack('2I', mat.shape[0], mat.shape[1])
binfile.write(header)
# then loop over columns and write each
for i in range(mat.shape[1]):
ddata = struct.pack('%id' % mat.shape[0], *mat[:,i])
binfile.write(ddata)
binfile.close()
if out_vdend:
# WRITE the dendritic response
nRep = len(data.vDdata)
mat = np.array(data.vDdata[0])
for i in range(1, nRep):
mat = np.hstack((mat, data.vDdata[i]))
bfname = './'+outdir+'/vDdata_'+filename+'.bin'
# create a binary file
binfile = file(bfname, 'wb')
# and write out two integers with the row and column dimension
header = struct.pack('2I', mat.shape[0], mat.shape[1])
binfile.write(header)
# then loop over columns and write each
for i in range(mat.shape[1]):
ddata = struct.pack('%id' % mat.shape[0], *mat[:,i])
binfile.write(ddata)
binfile.close()
#---------------------------------------------
# WRITE the location of the synapses
if (data.SYN):
if (data.GABA) :
Ilocs = np.array(data.Ilocs)
Ilocs[:,1] = 1 + Ilocs[:,1] # code that these are inhibitory synapses
Elocs = np.array(data.Elocs)
Locs = np.row_stack((Elocs, Ilocs))
else :
Locs = np.array(data.Elocs)
bfname = './'+outdir+'/synlocs_'+filename+'.bin'
# create a binary file
binfile = file(bfname, 'wb')
# and write out two integers with the row and column dimension
header = struct.pack('2I', Locs.shape[0], Locs.shape[1])
binfile.write(header)
# then loop over columns and write each
for i in range(Locs.shape[1]):
ddata = struct.pack('%id' % Locs.shape[0], *Locs[:,i])
binfile.write(ddata)
binfile.close()
#---------------------------------------------
# Write the input spike train
if (len(data.stim)>0):
stim = data.stim
bfname = './'+outdir+'/stim_'+filename+'.bin'
# create a binary file
binfile = file(bfname, 'wb')
# and write out two integers with the row and column dimension
header = struct.pack('2I', stim.shape[0], stim.shape[1])
binfile.write(header)
# then loop over columns and write each
for i in range(stim.shape[1]):
ddata = struct.pack('%id' % stim.shape[0], *stim[:,i])
binfile.write(ddata)
binfile.close()