def current(loci, ail):
cell = nm.NeuronModel(dl=1000, dd=2, sl=40, sd=20,\
hl=10, hd=2, ail=ail, aid=1.2, axl=1000, axd=1.2,\
dna=100, sna=100, hna=300, aina=4000, axna=300, dk=20,\
sk=20, hk=150, aik=1000, axk=150,\
na_type=7, aina12=1000, aina16=1000)
stim = nm.attach_current_clamp(cell, amp=1, dur=1)
vec = nm.set_recording_vectors(cell)
v = h.Vector()
v.record(cell.ais(loci)._ref_v)
icap = h.Vector() # (mA/cm2)
icap.record(cell.ais(loci)._ref_i_cap)
ileak = h.Vector() # (mA/cm2)
ileak.record(cell.ais(loci)._ref_i_pas)
ina = h.Vector() # (mA/cm2)
ina.record(cell.ais(loci)._ref_ina)
ik = h.Vector() # (mA/cm2)
ik.record(cell.ais(loci)._ref_ik)
nm.simulate()
print(nm.cal_velocity(vec, ["ais"]))
iax = -np.array(icap.to_python()) - np.array(ileak.to_python()) - np.array(
ina.to_python()) - np.array(ik.to_python())
cell.dend1 = None
cell.soma = None
cell.hill = None
cell.ais = None
cell.axon = None
for sec in h.allsec():
print(sec)
return v, icap, ileak, ina, ik, iax
if not os.path.exists(save_figure_path):
os.makedirs(save_figure_path)
for p in range(7):
bpv_sum = np.zeros([len(dist_x), len(len_x)])
fwv_sum = np.zeros([len(dist_x), len(len_x)])
for i, d in enumerate(dist_x):
for j, l in enumerate(len_x):
file_name = 'type'+str(na_type)\
+'_aina12_'+str(aina12_x[p])\
+'_aina16_'+str(aina16_x[p])\
+'_aik_'+str(aik_x[p])\
+'_dist_'+str(d)+'_len_'+str(l)+'_v_vec.p'
print(na_type, p, d, l)
cell = nm.NeuronModel(hl=int(d),\
ail=int(l),\
na_type=na_type,\
aina12=aina12_x[p],\
aina16=aina16_x[p],\
aik=aik_x[p])
stim = nm.attach_current_clamp(cell, amp=.7)
vec = nm.set_recording_vectors(cell)
nm.simulate()
nm.save_vec(vec, save_data_path + file_name)
nm.show_output(vec, save_figure_path + file_name)
bpv_sum[i, j], fwv_sum[i,
j], ___ = nm.cal_velocity(vec, ['ais'])
cell.dend1 = None
cell.dend2 = None
cell.soma = None
cell.hill = None
cell.ais = None
cell.axon = None
save_data_path = 'E:/02_AIS/Simulation/AIS/190708/fit_v2/data/aina' + str(
na12m) + str(na16m) + '/'
save_figure_path = 'E:/02_AIS/Simulation/AIS/190708/fit_v2/figure/aina' + str(
na12m) + str(na16m) + '/'
if not os.path.exists(save_data_path):
os.makedirs(save_data_path)
if not os.path.exists(save_figure_path):
os.makedirs(save_figure_path)
for i, gna in enumerate(ais_gna_x):
for j, l in enumerate(len_x):
print('na12m:', na12m, '; na16m:', na16m, ';gna:', gna,
'pS/um2; len:', l, 'um;')
cell = nm.NeuronModel(ail=int(l),
na_type=2,
na12_map=na12m,
na16_map=na16m,
aina12=gna,
aina16=gna)
stim = nm.attach_current_clamp(cell, amp=.7)
vec = nm.set_recording_vectors(cell)
nm.simulate()
nm.save_vec(
vec, save_data_path + str(na12m) + str(na16m) + '_gna_' +
str(gna) + '_len_' + str(l) + '_')
nm.show_output(
vec, save_figure_path + str(na12m) + str(na16m) + 'gna_' +
str(gna) + '_len_' + str(l) + '_')
bpv_len_sum[i, j], fwv_len_sum[i, j], ___ = nm.cal_velocity(
vec, ['ais'])
cell.dend1 = None
cell.dend2 = None
save_figure_path = 'E:/02_AIS/Simulation/AIS/190708/fit_v2/figure/3d_type_21/'
if not os.path.exists(save_data_path):
os.makedirs(save_data_path)
if not os.path.exists(save_figure_path):
os.makedirs(save_figure_path)
for p in range(7):
bpv_sum = np.zeros([len(dist_x), len(len_x)])
fwv_sum = np.zeros([len(dist_x), len(len_x)])
for i, d in enumerate(dist_x):
for j, l in enumerate(len_x):
print(p, d, l)
cell = nm.NeuronModel(hl=int(d), \
ail=int(l), \
na_type=2, \
na12_map=1,
na16_map=1,
aina12=aina12_x[p],\
aina16=aina16_x[p],\
aik=aik_x[p])
stim = nm.attach_current_clamp(cell, amp=.7)
vec = nm.set_recording_vectors(cell)
nm.simulate()
nm.save_vec(vec, save_data_path\
+'type2_map1'\
+'_aina12_'+str(aina12_x[p])\
+'_aina16_'+str(aina16_x[p])\
+'_aik_'+str(aik_x[p])\
+'_dist_'+str(d)+'_len_'+str(l)+'_')
nm.show_output(vec, save_figure_path\
+'type2_map1'\
+'_aina12_'+str(aina12_x[p])\
dist_step = 5
dist_stop = 33
dist_x = np.arange(dist_start, dist_stop + 1, dist_step)
bpv_len_sum = np.zeros([len(hill_gna_x), len(len_x)])
fwv_len_sum = np.zeros([len(hill_gna_x), len(len_x)])
bpv_dist_sum = np.zeros([len(hill_gna_x), len(dist_x)])
fwv_dist_sum = np.zeros([len(hill_gna_x), len(dist_x)])
save_data_path = 'E:/02_AIS/Simulation/AIS/190708/fit_v2/data/hna/'
save_figure_path = 'E:/02_AIS/Simulation/AIS/190708/fit_v2/figure/hna/'
for i, gna in enumerate(hill_gna_x):
for j, l in enumerate(len_x):
print('hna:', gna, 'pS/um2; len:', l, 'um;')
cell = nm.NeuronModel(ail=int(l), na_type=1, hna=gna)
stim = nm.attach_current_clamp(cell, amp=.7)
vec = nm.set_recording_vectors(cell)
nm.simulate()
nm.save_vec(
vec, save_data_path + 'hna_' + str(gna) + '_len_' + str(l) + '_')
nm.show_output(
vec, save_figure_path + 'hna_' + str(gna) + '_len_' + str(l) + '_')
bpv_len_sum[i, j], fwv_len_sum[i,
j], ___ = nm.cal_velocity(vec, ['ais'])
cell.dend1 = None
cell.dend2 = None
cell.soma = None
cell.hill = None
cell.ais = None
cell.axon = None
save_data_path = 'E:/02_AIS/Simulation/AIS/190708/fit_v2/data/3d_type_1/'
save_figure_path = 'E:/02_AIS/Simulation/AIS/190708/fit_v2/figure/3d_type_1/'
if not os.path.exists(save_data_path):
os.makedirs(save_data_path)
if not os.path.exists(save_figure_path):
os.makedirs(save_figure_path)
for p in aina_x + [aina]:
bpv_sum = np.zeros([len(dist_x), len(len_x)])
fwv_sum = np.zeros([len(dist_x), len(len_x)])
for i, d in enumerate(dist_x):
for j, l in enumerate(len_x):
print(p, d, l)
cell = nm.NeuronModel(hl=int(d), \
ail=int(l), \
na_type=1, \
aina=p,\
aik=aik)
stim = nm.attach_current_clamp(cell, amp=.7)
vec = nm.set_recording_vectors(cell)
nm.simulate()
nm.save_vec(
vec, save_data_path + 'aina_' + str(p) + '_aik_' + str(aik) +
'_dist_' + str(d) + '_len_' + str(l) + '_')
nm.show_output(
vec, save_figure_path + 'aina_' + str(p) + '_aik_' + str(aik) +
'_dist_' + str(d) + '_len_' + str(l) + '_')
bpv_sum[i, j], fwv_sum[i, j], ___ = nm.cal_velocity(vec, ['ais'])
cell.dend1 = None
cell.dend2 = None
cell.soma = None
for p in range(7):
bpv_sum = np.zeros([len(dist_x), len(len_x)])
fwv_sum = np.zeros([len(dist_x), len(len_x)])
for i, d in enumerate(dist_x):
for j, l in enumerate(len_x):
print(p, d, l)
file_name = 'aina12_'+str(aina12_x[p])\
+'_aina16_'+str(aina16_x[p])\
+'_aik_'+str(aik_x[p])\
+'_dist_'+str(d)\
+'_len_'+str(l)+'_'
cell = nm.NeuronModel(hl=int(d), \
ail=int(l), \
na_type=na_type_i, \
aina12=aina12_x[p],\
aina16=aina16_x[p],\
aik=aik_x[p],\
hna = hna_i,\
hk = hk_i,\
hd = hd_i)
stim = nm.attach_current_clamp(cell, amp=.7)
vec = nm.set_recording_vectors(cell)
nm.simulate()
nm.save_vec(vec, save_data_path + file_name)
nm.show_output(vec, save_figure_path + file_name)
bpv_sum[i, j], fwv_sum[i, j], ___ = nm.cal_velocity(vec, ['ais'])
cell.dend1 = None
cell.dend2 = None
cell.soma = None
cell.hill = None
cell.ais = None
bpv_len_sum = np.zeros([len(hill_gk_x), len(len_x)])
fwv_len_sum = np.zeros([len(hill_gk_x), len(len_x)])
bpv_dist_sum = np.zeros([len(hill_gk_x), len(dist_x)])
fwv_dist_sum = np.zeros([len(hill_gk_x), len(dist_x)])
save_data_path = 'E:/02_AIS/Simulation/AIS/190708/fit_v2/data/hk/'
save_figure_path = 'E:/02_AIS/Simulation/AIS/190708/fit_v2/figure/hk/'
if not os.path.exists(save_data_path):
os.makedirs(save_data_path)
if not os.path.exists(save_figure_path):
os.makedirs(save_figure_path)
for i,gk in enumerate(hill_gk_x):
for j, l in enumerate(len_x):
print('hk:', gk, 'pS/um2; len:', l,'um;')
cell = nm.NeuronModel(ail=int(l), na_type=1, hk=gk)
stim = nm.attach_current_clamp(cell, amp=.7)
vec = nm.set_recording_vectors(cell)
nm.simulate()
nm.save_vec(vec, save_data_path+'hk_'+str(gk)+'_len_'+str(l)+'_')
nm.show_output(vec, save_figure_path+'hk_'+str(gk)+'_len_'+str(l)+'_')
bpv_len_sum[i,j], fwv_len_sum[i,j], ___ = nm.cal_velocity(vec, ['ais'])
cell.dend1 = None
cell.dend2 = None
cell.soma = None
cell.hill = None
cell.ais = None
cell.axon = None
for sec in h.allsec():
print(sec)
save_data_path = 'E:/02_AIS/Simulation/AIS/190708/fit_v2/data/aik' + str(
na12m) + str(na16m) + '/'
save_figure_path = 'E:/02_AIS/Simulation/AIS/190708/fit_v2/figure/aik' + str(
na12m) + str(na16m) + '/'
if not os.path.exists(save_data_path):
os.makedirs(save_data_path)
if not os.path.exists(save_figure_path):
os.makedirs(save_figure_path)
for i, gk in enumerate(ais_gk_x):
for j, l in enumerate(len_x):
print('na12m:', na12m, '; na16m:', na16m, ';gk:', gk,
'pS/um2; len:', l, 'um;')
cell = nm.NeuronModel(ail=int(l),
na_type=2,
na12_map=na12m,
na16_map=na16m,
aik=gk)
stim = nm.attach_current_clamp(cell, amp=.7)
vec = nm.set_recording_vectors(cell)
nm.simulate()
nm.save_vec(
vec, save_data_path + str(na12m) + str(na16m) + '_gk_' +
str(gk) + '_len_' + str(l) + '_')
nm.show_output(
vec, save_figure_path + str(na12m) + str(na16m) + 'gk_' +
str(gk) + '_len_' + str(l) + '_')
bpv_len_sum[i, j], fwv_len_sum[i, j], ___ = nm.cal_velocity(
vec, ['ais'])
cell.dend1 = None
cell.dend2 = None
@author: cbi
"""
import neuronmodel as nm
import numpy as np
from matplotlib import pyplot as plt
from neuron import h
import os
import pickle
plt.close('all')
seg = ['ais']
cell = nm.NeuronModel(dl=1000, dd=2, sl=40, sd=20, \
hl=10, hd=2, ail=40, aid=1.2, axl=1000, axd=1.2,\
dna=100, sna=100, hna=300, aina=4000, axna=300, dk=20, \
sk=20, hk=150, aik=1000, axk=150,\
na_type=6, aina12=6000, aina16=6000)
stim = nm.attach_current_clamp(cell, amp=1.0)
vec = nm.set_recording_vectors(cell)
nm.simulate()
nm.show_output(vec, None)
bpv, fwv, __ = nm.cal_velocity(vec, seg)
print('normal:', bpv, fwv)
cell.dend1 = None
cell.soma = None
cell.hill = None
cell.ais = None
cell.axon = None
for sec in h.allsec():
print(sec)
aik_i = 3000 hna_i = 300 hk_i = 200 hl_i = 15 ail_i = 40 hd_i = 4 for j, l in enumerate(len_x): print(j,l) cell = nm.NeuronModel(ail=int(l), \ na_type=na_type_i, \ na12_map=na12_map_i, \ na16_map=na16_map_i, \ aina12=aina12_i, \ aina16=aina16_i, \ aik=aik_i,\ hna=hna_i,\ hk=hk_i,\ hl = hl_i,\ hd = hd_i) stim = nm.attach_current_clamp(cell, amp=.7) vec = nm.set_recording_vectors(cell) nm.simulate() nm.show_output(vec, save_figure_log_path+'len_'+str(l)+'_') bpv_len_sum[0,j], fwv_len_sum[0,j], ___ = nm.cal_velocity(vec, ['ais']) cell.dend1 = None cell.dend2 = None cell.soma = None cell.hill = None cell.ais = None