def passive_test (tree, v_init):
cell = cells.make_neurotree_cell ("MOPPCell", neurotree_dict=tree)
h.dt = 0.025
prelength = 1000
mainlength = 2000
tstop = prelength+mainlength
stimdur = 500.0
stim1 = h.IClamp(cell.sections[0](0.5))
stim1.delay = prelength
stim1.dur = stimdur
stim1.amp = -0.1
h.tlog = h.Vector()
h.tlog.record (h._ref_t)
h.Vlog = h.Vector()
h.Vlog.record (cell.sections[0](0.5)._ref_v)
h.tstop = tstop
utils.simulate(h, v_init, prelength,mainlength)
## compute membrane time constant
vrest = h.Vlog.x[int(h.tlog.indwhere(">=",prelength-1))]
vmin = h.Vlog.min()
vmax = vrest
## the time it takes the system's step response to reach 1-1/e (or
## 63.2%) of the peak value
amp23 = 0.632 * abs (vmax - vmin)
vtau0 = vrest - amp23
tau0 = h.tlog.x[int(h.Vlog.indwhere ("<=", vtau0))] - prelength
f=open("MOPPCell_passive_results.dat",'w')
f.write ("DC input resistance: %g MOhm\n" % h.rn(cell))
f.write ("vmin: %g mV\n" % vmin)
f.write ("vtau0: %g mV\n" % vtau0)
f.write ("tau0: %g ms\n" % tau0)
f.close()
def ap_rate_test (tree, v_init):
cell = cells.make_neurotree_cell ("MOPPCell", neurotree_dict=tree)
h.dt = 0.025
prelength = 1000.0
mainlength = 2000.0
tstop = prelength+mainlength
stimdur = 1000.0
stim1 = h.IClamp(cell.sections[0](0.5))
stim1.delay = prelength
stim1.dur = stimdur
stim1.amp = 0.2
h.tlog = h.Vector()
h.tlog.record (h._ref_t)
h.Vlog = h.Vector()
h.Vlog.record (cell.sections[0](0.5)._ref_v)
h.spikelog = h.Vector()
nc = h.NetCon(cell.sections[0](0.5)._ref_v, h.nil)
nc.threshold = -40.0
nc.record(h.spikelog)
h.tstop = tstop
it = 1
## Increase the injected current until at least 60 spikes occur
## or up to 5 steps
while (h.spikelog.size() < 60):
utils.simulate(h, v_init, prelength,mainlength)
if ((h.spikelog.size() < 50) & (it < 5)):
print("ap_rate_test: stim1.amp = %g spikelog.size = %d\n" % (stim1.amp, h.spikelog.size()))
stim1.amp = stim1.amp + 0.1
h.spikelog.clear()
h.tlog.clear()
h.Vlog.clear()
it += 1
else:
break
print("ap_rate_test: stim1.amp = %g spikelog.size = %d\n" % (stim1.amp, h.spikelog.size()))
isivect = h.Vector(h.spikelog.size()-1, 0.0)
tspike = h.spikelog.x[0]
for i in range(1,int(h.spikelog.size())):
isivect.x[i-1] = h.spikelog.x[i]-tspike
tspike = h.spikelog.x[i]
print("ap_rate_test: isivect.size = %d\n" % isivect.size())
isimean = isivect.mean()
isivar = isivect.var()
isistdev = isivect.stdev()
isilast = int(isivect.size())-1
if (isivect.size() > 10):
isi10th = 10
else:
isi10th = isilast
## Compute the last spike that is largest than the first one.
## This is necessary because some variants of the model generate spike doublets,
## (i.e. spike with very short distance between them, which confuse the ISI statistics.
isilastgt = int(isivect.size())-1
while (isivect.x[isilastgt] < isivect.x[1]):
isilastgt = isilastgt-1
if (not (isilastgt > 0)):
isivect.printf()
raise RuntimeError("Unable to find ISI greater than first ISI: forest_path = %s gid = %d" % (forest_path, gid))
f=open("MOPPCell_ap_rate_results.dat",'w')
f.write ("## number of spikes: %g\n" % h.spikelog.size())
f.write ("## FR mean: %g\n" % (1.0 / isimean))
f.write ("## ISI mean: %g\n" % isimean)
f.write ("## ISI variance: %g\n" % isivar)
f.write ("## ISI stdev: %g\n" % isistdev)
f.write ("## ISI adaptation 1: %g\n" % (old_div(isivect.x[0], isimean)))
f.write ("## ISI adaptation 2: %g\n" % (old_div(isivect.x[0], isivect.x[isilast])))
f.write ("## ISI adaptation 3: %g\n" % (old_div(isivect.x[0], isivect.x[isi10th])))
f.write ("## ISI adaptation 4: %g\n" % (old_div(isivect.x[0], isivect.x[isilastgt])))
f.close()
f=open("MOPPCell_voltage_trace.dat",'w')
for i in range(0, int(h.tlog.size())):
f.write('%g %g\n' % (h.tlog.x[i], h.Vlog.x[i]))
f.close()