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"""
Serial batch implementation
for stimulus current i iterating over a range of values
run a simulation and report spike frequency
save i & corresponding f to a file
optionally plot fi curve
"""
from neuron import h, gui
import time
# Simulation parameters
PLOTRESULTS = True # if True, generates a graph that shows f-i curve
h.tstop = 500 # ms, more than long enough for 15 spikes at ISI = 25 ms
AMP0 = 0.1 # nA -- minimum stimulus
D_AMP = 0.02 # nA -- stim increment between runs
NRUNS = 30
# model specification
from cell import Cell
cell = Cell()
# instrumentation
# experimental manipulations
stim = h.IClamp(cell.soma(0.5))
stim.delay = 1 # ms
stim.dur = 1e9
stim.amp = 0.1 # nA
def fstimamp(run_id):
"""takes run_id, returns corresponding stimulus amplitude"""
return AMP0 + run_id * D_AMP
def set_params(run_id):
"""sets stimulus amplitude to appropriate value for this run"""
stim.amp = fstimamp(run_id)
# data recording and analysis
# count only those spikes that get to distal end of dend
nc = h.NetCon(cell.dend(1)._ref_v, None, sec=cell.dend)
nc.threshold = -10 # mV
spvec = h.Vector()
nc.record(spvec)
NSETTLE = 5 # ignore the first NSETTLE ISI (allow freq to stablize)
NINVL = 10 # num ISI from which frequency will be calculated
NMIN = NSETTLE + NINVL # ignore recordings with fewer than this num of ISIs
def get_frequency(spvec):
nspikes = spvec.size()
if nspikes > NMIN:
t2 = spvec[-1] # last spike
t1 = spvec[-(1 + NINVL)] # NINVL prior to last spike
return NINVL * 1.0e3 / (t2 - t1)
else:
return 0
#
# Simulation control
#
# batch control
t_start = time.time() # keep track of execution time past this point
def batchrun(n):
stims = []
freqs = []
for run_id in range(n):
set_params(run_id)
h.run()
stims.append(stim.amp)
freqs.append(get_frequency(spvec))
print("Finished %d of %d." % (run_id + 1, n))
return stims, freqs
stims, freqs = batchrun(NRUNS)
#
# Reporting of results
#
def save_results(filename, stims, freqs):
with open(filename, "w") as f:
f.write("label:%s\n%d\n" % ("f", len(freqs)))
for stim, freq in zip(stims, freqs):
f.write("%g\t%g\n" % (stim, freq))
save_results("fi.dat", stims, freqs)
print("%g seconds" % (time.time() - t_start)) # report run time
if PLOTRESULTS:
from plotfi import plotfi
fig = plotfi(stims, freqs)
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