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
cell.soma = None
cell.hill = None
cell.ais = None
cell.axon = None
for sec in h.allsec():
print(sec)
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
for sec in h.allsec():
print(sec)
with open(save_data_path+'type'+str(na_type)\
+'_aina12_'+str(aina12_x[p])\
+'_aina16_'+str(aina16_x[p])\
+'_aik_'+str(aik_x[p])\
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])\
+'_aina16_'+str(aina16_x[p])\
+'_aik_'+str(aik_x[p])\
+'_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
cell.hill = None
cell.ais = None
cell.axon = None
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
for sec in h.allsec():
print(sec)
for j, d in enumerate(dist_x):
print('hna:', gna, 'pS/um2; dist:', d, 'um;')
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
cell.hill = None
cell.ais = None
cell.axon = None
for sec in h.allsec():
print(sec)
with open(
save_data_path + 'aina_' + str(p) + '_aik_' + str(aik) + '_bpv.p',
save_data_path = 'E:/02_AIS/Simulation/AIS/190708/fit_v2/data/aik/'
save_figure_path = 'E:/02_AIS/Simulation/AIS/190708/fit_v2/figure/aik/'
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('gk:', gk, 'pS/um2; len:', l,'um;')
cell = nm.NeuronModel(ail=int(l), na_type=1, 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+'gk_'+str(gk)+'_len_'+str(l)+'_')
nm.show_output(vec, save_figure_path+'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
cell.soma = None
cell.hill = None
cell.ais = None
cell.axon = None
for sec in h.allsec():
print(sec)
for j, d in enumerate(dist_x):
print('gk:', gk, 'pS/um2; dist:', d,'um;')
cell = nm.NeuronModel(hl=int(d), na_type=1, aik=gk)
stim = nm.attach_current_clamp(cell, amp=.7)
cell = nm.NeuronModel(ail=int(l), \
na_type=na_type_i, \
# na12_map=na12_map_i, \
# na16_map=na16_map_i, \
aina12=aina12_ii, \
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.save_vec(
vec, save_data_path + 'aina12_' + str(aina12_ii) + '_dist_' +
str(hl_i) + '_len_' + str(l) + '_')
nm.show_output(
vec, save_figure_path + 'aina12_' + str(aina12_ii) + '_dist_' +
str(hl_i) + '_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)