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
na16_map_i = 4
aina12_i = 5000
aina16_i = 12000
na_type_i = 6
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):
with open(data_path + 'dist_' + str(hl_i) + '_len_' + str(l) + '_v_vec.p',
'rb') as file:
vec = pickle.load(file)
bpv_len_sum[0, j], fwv_len_sum[0, j], ___ = nm.cal_velocity(vec, ['ais'])
for j, d in enumerate(dist_x):
with open(data_path + 'dist_' + str(d) + '_len_' + str(ail_i) + '_v_vec.p',
'rb') as file:
vec = pickle.load(file)
bpv_dist_sum[0, j], fwv_dist_sum[0, j], ___ = nm.cal_velocity(vec, ['ais'])
# best param
title = 'na12_map = ' + str(na12_map_i) + ' na16_map = ' + str(
na16_map_i) + '\n\
aina12 = ' + str(aina12_i) + ' aina16 = ' + str(aina16_i) + '\n\
na_type = ' + str(na_type_i) + ' aik = ' + str(aik_i) + '\n\
hna = ' + str(hna_i) + ' hk = ' + str(hk_i) + '\n\
hl = ' + str(hl_i) + ' ail = ' + str(ail_i)
nm.show_data_fit(bpv_len_sum, fwv_len_sum, bpv_dist_sum, fwv_dist_sum, title)
+'_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])\
+'_bpv.p', 'wb') as file:
pickle.dump(bpv_sum, file)
with open(save_data_path+'type'+str(na_type)\
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
for sec in h.allsec():
print(sec)
with open(save_data_path+'type2_map1'\
+'_aina12_'+str(aina12_x[p])\
+'_aina16_'+str(aina16_x[p])\
+'_aik_'+str(aik_x[p])\
+'_bpv.p', 'wb') as file:
pickle.dump(bpv_sum, file)
with open(save_data_path+'type2_map1'\
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)
cell = nm.NeuronModel(dl=1000, dd=2, sl=40, sd=20,\
hl=10, hd=2, ail=60, 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=500, axk=150,\
na_type=8, aina12=1800, aina16=1800)
