# "Biophysical modeling of the whole-cell dynamics of C. elegans motor and interneurons families"
# M. Nicoletti et al. PloS ONE, 19(3): e0298105.
# https://doi.org/10.1371/journal.pone.0298105
def RIM_simulation_iclamp(gRIM_scaled,s1,s2,ns):
from neuron import h,gui
import numpy
import math
from operator import add
surf=103.34e-8 # surface in cm^2 form neuromorpho RIML
vol=10.94e-12 # total volume
L=math.sqrt(surf/math.pi)
rsoma=L*1e4
cm_uFcm2=gRIM_scaled[8]
soma=h.Section(name="soma")
soma.L=rsoma
soma.diam=rsoma
soma.Ra=100
soma.cm=cm_uFcm2
h.psection(sec=soma)
soma.insert('shl1')
soma.insert('egl2')
soma.insert('irk')
soma.insert('cca1')
soma.insert('unc2')
soma.insert('egl19')
soma.insert('leak')
for seg in soma:
seg.shl1.gbar=gRIM_scaled[0]
seg.egl2.gbar=gRIM_scaled[1]
seg.irk.gbar=gRIM_scaled[2]
seg.cca1.gbar=gRIM_scaled[3]
seg.unc2.gbar=gRIM_scaled[4]
seg.egl19.gbar=gRIM_scaled[5]
seg.leak.gbar=gRIM_scaled[6]
seg.leak.e=gRIM_scaled[7]
seg.eca=60
seg.ek=-80
stim=h.IClamp(soma(0.5))
dir(stim)
stim.delay=5000
stim.amp=10
stim.dur=5000
v_vec = h.Vector()
t_vec = h.Vector() # Time stamp vector
v_vec.record(soma(0.5)._ref_v)
t_vec.record(h._ref_t)
simdur =14000
ref_v=[]
ref_t=[]
num_step=11
for i in numpy.linspace(start=s1, stop=s2, num=ns):
stim.amp=i
h.tstop=simdur
h.dt=0.04
h.finitialize(-60)
h.run()
ref_t_vec=numpy.zeros_like(t_vec)
t_vec.to_python(ref_t_vec)
ref_t.append(ref_t_vec)
ref_v_vec=numpy.zeros_like(v_vec)
v_vec.to_python(ref_v_vec)
ref_v.append(ref_v_vec)
# total current calculation
v=[]
v=numpy.array(list(ref_v))
time1=numpy.array(ref_t)
length=time1.shape
# cut the initial transient
dd=numpy.amax(numpy.where(time1[1,:]<4000))
time=time1[:,dd:length[1]]-4000
volt=v[:, dd:length[1]]
## CALCULATION OF STEADY-STATE CURRENT-VOLATGE RELATION
ind=numpy.where(numpy.logical_and(time[0]>=5990, time[0]<=6010))
ind_max=numpy.amax(ind)
ind_min=numpy.amin(ind)
iv=numpy.mean(volt[:,ind_min:ind_max],axis=1)
# CALCULATION OF PEAK CURRENT-VOLTAGE RELATION (as in Ramot et al 2008)
ind2=numpy.where(numpy.logical_and(time[0]>=1000, time[0]<=1100))
ind2_max=numpy.amax(ind2)
ind2_min=numpy.amin(ind2)
iv_peak=numpy.amax(volt[:,ind2_min:ind2_max])
iv_peak=[]
for j in range(ns):
if j<=3:
peak=numpy.amin(volt[j,ind2_min:ind2_max])
else:
peak=numpy.amax(volt[j,ind2_min:ind2_max])
iv_peak.append(peak)
return volt, time, iv_peak, iv