def dkdt_syn_both(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
phi_sn, phi_se, phi_sg, phi_dn, phi_de, phi_dg, phi_msn, phi_mdn, phi_msg, phi_mdg = my_cell.membrane_potentials(
)
E_Na_sn, E_Na_sg, E_Na_dn, E_Na_dg, E_K_sn, E_K_sg, E_K_dn, E_K_dg, E_Cl_sn, E_Cl_sg, E_Cl_dn, E_Cl_dg, E_Ca_sn, E_Ca_dn = my_cell.reversal_potentials(
)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > syn_start:
dNadt_sn, dNadt_se, dKdt_sn, dKdt_se, dCadt_sn, dCadt_se = activate_synapse(
my_cell, t, spikes, phi_msn, E_Na_sn, E_K_sn, E_Ca_sn,
dNadt_sn, dNadt_se, dKdt_sn, dKdt_se, dCadt_sn, dCadt_se)
dNadt_dn, dNadt_de, dKdt_dn, dKdt_de, dCadt_dn, dCadt_de = activate_synapse(
my_cell, t, spikes, phi_mdn, E_Na_dn, E_K_dn, E_Ca_dn,
dNadt_dn, dNadt_de, dKdt_dn, dKdt_de, dCadt_dn, dCadt_de)
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
def dkdt_Cl_both(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > stim_start and t < stim_end:
dCldt_sn -= I_stim / my_cell.F
dCldt_se += I_stim / my_cell.F
dCldt_dn -= I_stim / my_cell.F
dCldt_de += I_stim / my_cell.F
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
def dummy_cell():
""" Create "dummy" cell to fetch functions and paramters. """
T = 309.14
alpha = 2
cbK_se = 3.082
cbK_sg = 99.959
cbK_de = 3.082
cbK_dg = 99.959
cbCa_sn = 0.01
cbCa_dn = 0.01
cell = edNEGmodel(T, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, \
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, alpha, \
cbK_se, cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, 1, 1, 1, 1, 1, 1, \
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
return cell
def solve_edNEGmodel(t_dur, alpha, syn_rate, syn_start, syn_end, syn_protocol):
"""
Solves the edNEG model (Sætra et al. 2021) using the solve_ivp function from scipy.
Arguments:
t_dur (float): duration of simulation [s]
alpha (float): coupling strength
I_stim (float): stimulus current [A]
stim_start (float): time of stimulus onset [s]
stim_end (float): time of stimulus offset [s]
syn_protocol (str): protocol defining synaptic location
Returns:
sol: solution from solve_ivp
my_cell: edNEGmodel object
spikes: synaptic spike train
"""
spikes = homogeneous_poisson_process(rate=syn_rate * qnt.Hz,
t_start=syn_start * qnt.s,
t_stop=syn_end * qnt.s,
as_array=True)
filename = pkg_resources.resource_filename(
'data', 'initial_values/initial_values.npz')
data = np.load(filename)
T = 309.14
# initial membrane potentials [V]
phi_msn = data['phi_msn']
phi_msg = data['phi_msg']
phi_mdn = data['phi_mdn']
phi_mdg = data['phi_mdg']
# initial volumes [m**3]
V_sn0 = data['V_sn']
V_se0 = data['V_se']
V_sg0 = data['V_sg']
V_dn0 = data['V_dn']
V_de0 = data['V_de']
V_dg0 = data['V_dg']
# initial amounts of ions [mol]
Na_sn0 = data['Na_sn']
Na_se0 = data['Na_se']
Na_sg0 = data['Na_sg']
K_sn0 = data['K_sn']
K_se0 = data['K_se']
K_sg0 = data['K_sg']
Cl_sn0 = data['Cl_sn']
Cl_se0 = data['Cl_se']
Cl_sg0 = data['Cl_sg']
Ca_sn0 = data['Ca_sn']
Ca_se0 = data['Ca_se']
Na_dn0 = data['Na_dn']
Na_de0 = data['Na_de']
Na_dg0 = data['Na_dg']
K_dn0 = data['K_dn']
K_de0 = data['K_de']
K_dg0 = data['K_dg']
Cl_dn0 = data['Cl_dn']
Cl_de0 = data['Cl_de']
Cl_dg0 = data['Cl_dg']
Ca_dn0 = data['Ca_dn']
Ca_de0 = data['Ca_de']
# intial gating variables
n0 = data['n']
h0 = data['h']
s0 = data['s']
c0 = data['c']
q0 = data['q']
z0 = data['z']
# baseline ion concentrations [mol/m**3]
cbK_se = 3.082
cbK_sg = 99.959
cbK_de = 3.082
cbK_dg = 99.959
cbCa_sn = 0.01
cbCa_dn = 0.01
# residual charges [mol]
res_sn = phi_msn * 3e-2 * 616e-12 / 9.648e4
res_sg = phi_msg * 3e-2 * 616e-12 / 9.648e4
res_se = res_sn + res_sg
res_dn = phi_mdn * 3e-2 * 616e-12 / 9.648e4
res_dg = phi_mdg * 3e-2 * 616e-12 / 9.648e4
res_de = res_dn + res_dg
X_sn = Na_sn0 + K_sn0 - Cl_sn0 + 2 * Ca_sn0 - res_sn
X_se = Na_se0 + K_se0 - Cl_se0 + 2 * Ca_se0 + res_se
X_sg = Na_sg0 + K_sg0 - Cl_sg0 - res_sg
X_dn = Na_dn0 + K_dn0 - Cl_dn0 + 2 * Ca_dn0 - res_dn
X_de = Na_de0 + K_de0 - Cl_de0 + 2 * Ca_de0 + res_de
X_dg = Na_dg0 + K_dg0 - Cl_dg0 - res_dg
# residual mass [mol/m**3]
cM_sn = (Na_sn0 + K_sn0 + Cl_sn0 + Ca_sn0) / V_sn0
cM_se = (Na_se0 + K_se0 + Cl_se0 + Ca_se0) / V_se0
cM_sg = (Na_sg0 + K_sg0 + Cl_sg0) / V_sg0
cM_dn = (Na_dn0 + K_dn0 + Cl_dn0 + Ca_dn0) / V_dn0
cM_de = (Na_de0 + K_de0 + Cl_de0 + Ca_de0) / V_de0
cM_dg = (Na_dg0 + K_dg0 + Cl_dg0) / V_dg0
# print initial values
#init_cell = edNEGmodel(T, Na_sn0, Na_se0, Na_sg0, Na_dn0, Na_de0, Na_dg0, K_sn0, K_se0, K_sg0, K_dn0, K_de0, K_dg0, Cl_sn0, Cl_se0, Cl_sg0, Cl_dn0, Cl_de0, Cl_dg0, Ca_sn0, Ca_se0, Ca_dn0, Ca_de0, X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se, cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n0, h0, s0, c0, q0, z0, V_sn0, V_se0, V_sg0, V_dn0, V_de0, V_dg0, cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
#print_initial_values(init_cell)
# define differential equations for different synaptic protocols
def dkdt_syn_soma(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
phi_sn, phi_se, phi_sg, phi_dn, phi_de, phi_dg, phi_msn, phi_mdn, phi_msg, phi_mdg = my_cell.membrane_potentials(
)
E_Na_sn, E_Na_sg, E_Na_dn, E_Na_dg, E_K_sn, E_K_sg, E_K_dn, E_K_dg, E_Cl_sn, E_Cl_sg, E_Cl_dn, E_Cl_dg, E_Ca_sn, E_Ca_dn = my_cell.reversal_potentials(
)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > syn_start:
dNadt_sn, dNadt_se, dKdt_sn, dKdt_se, dCadt_sn, dCadt_se = activate_synapse(
my_cell, t, spikes, phi_msn, E_Na_sn, E_K_sn, E_Ca_sn,
dNadt_sn, dNadt_se, dKdt_sn, dKdt_se, dCadt_sn, dCadt_se)
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
def dkdt_syn_dendrite(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
phi_sn, phi_se, phi_sg, phi_dn, phi_de, phi_dg, phi_msn, phi_mdn, phi_msg, phi_mdg = my_cell.membrane_potentials(
)
E_Na_sn, E_Na_sg, E_Na_dn, E_Na_dg, E_K_sn, E_K_sg, E_K_dn, E_K_dg, E_Cl_sn, E_Cl_sg, E_Cl_dn, E_Cl_dg, E_Ca_sn, E_Ca_dn = my_cell.reversal_potentials(
)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > syn_start:
dNadt_dn, dNadt_de, dKdt_dn, dKdt_de, dCadt_dn, dCadt_de = activate_synapse(
my_cell, t, spikes, phi_mdn, E_Na_dn, E_K_dn, E_Ca_dn,
dNadt_dn, dNadt_de, dKdt_dn, dKdt_de, dCadt_dn, dCadt_de)
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
def dkdt_syn_both(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
phi_sn, phi_se, phi_sg, phi_dn, phi_de, phi_dg, phi_msn, phi_mdn, phi_msg, phi_mdg = my_cell.membrane_potentials(
)
E_Na_sn, E_Na_sg, E_Na_dn, E_Na_dg, E_K_sn, E_K_sg, E_K_dn, E_K_dg, E_Cl_sn, E_Cl_sg, E_Cl_dn, E_Cl_dg, E_Ca_sn, E_Ca_dn = my_cell.reversal_potentials(
)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > syn_start:
dNadt_sn, dNadt_se, dKdt_sn, dKdt_se, dCadt_sn, dCadt_se = activate_synapse(
my_cell, t, spikes, phi_msn, E_Na_sn, E_K_sn, E_Ca_sn,
dNadt_sn, dNadt_se, dKdt_sn, dKdt_se, dCadt_sn, dCadt_se)
dNadt_dn, dNadt_de, dKdt_dn, dKdt_de, dCadt_dn, dCadt_de = activate_synapse(
my_cell, t, spikes, phi_mdn, E_Na_dn, E_K_dn, E_Ca_dn,
dNadt_dn, dNadt_de, dKdt_dn, dKdt_de, dCadt_dn, dCadt_de)
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
# solve
t_span = (0, t_dur)
k0 = [
Na_sn0, Na_se0, Na_sg0, Na_dn0, Na_de0, Na_dg0, K_sn0, K_se0, K_sg0,
K_dn0, K_de0, K_dg0, Cl_sn0, Cl_se0, Cl_sg0, Cl_dn0, Cl_de0, Cl_dg0,
Ca_sn0, Ca_se0, Ca_dn0, Ca_de0, n0, h0, s0, c0, q0, z0, V_sn0, V_se0,
V_sg0, V_dn0, V_de0, V_dg0
]
if syn_protocol == 'syn_soma':
sol = solve_ivp(dkdt_syn_soma,
t_span,
k0,
method='RK23',
max_step=1e-4)
elif syn_protocol == 'syn_dendrite':
sol = solve_ivp(dkdt_syn_dendrite,
t_span,
k0,
method='RK23',
max_step=1e-4)
elif syn_protocol == 'syn_both':
sol = solve_ivp(dkdt_syn_both,
t_span,
k0,
method='RK23',
max_step=1e-4)
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = sol.y
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, \
Ca_sn, Ca_se, Ca_dn, Ca_de, X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, \
cbK_se, cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s, c, q, z, \
V_sn, V_se, V_sg, V_dn, V_de, V_dg, cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
return sol, my_cell, spikes
def solve_edNEGmodel(t_dur, alpha, I_stim, stim_start, stim_end,
injection_protocol):
"""
Solves the edNEG model (Sætra et al. 2021) using the solve_ivp function from scipy.
Arguments:
t_dur (float): duration of simulation [s]
alpha (float): coupling strength
I_stim (float): stimulus current [A]
stim_start (float): time of stimulus onset [s]
stim_end (float): time of stimulus offset [s]
injection_protocol (str): injection protocol defining ion species and location
Returns:
sol: solution from solve_ivp
my_cell: edNEGmodel object
"""
filename = pkg_resources.resource_filename(
'data', 'initial_values/initial_values.npz')
data = np.load(filename)
T = 309.14
# initial membrane potentials [V]
phi_msn = data['phi_msn']
phi_msg = data['phi_msg']
phi_mdn = data['phi_mdn']
phi_mdg = data['phi_mdg']
# initial volumes [m**3]
V_sn0 = data['V_sn']
V_se0 = data['V_se']
V_sg0 = data['V_sg']
V_dn0 = data['V_dn']
V_de0 = data['V_de']
V_dg0 = data['V_dg']
# initial amounts of ions [mol]
Na_sn0 = data['Na_sn']
Na_se0 = data['Na_se']
Na_sg0 = data['Na_sg']
K_sn0 = data['K_sn']
K_se0 = data['K_se']
K_sg0 = data['K_sg']
Cl_sn0 = data['Cl_sn']
Cl_se0 = data['Cl_se']
Cl_sg0 = data['Cl_sg']
Ca_sn0 = data['Ca_sn']
Ca_se0 = data['Ca_se']
Na_dn0 = data['Na_dn']
Na_de0 = data['Na_de']
Na_dg0 = data['Na_dg']
K_dn0 = data['K_dn']
K_de0 = data['K_de']
K_dg0 = data['K_dg']
Cl_dn0 = data['Cl_dn']
Cl_de0 = data['Cl_de']
Cl_dg0 = data['Cl_dg']
Ca_dn0 = data['Ca_dn']
Ca_de0 = data['Ca_de']
# intial gating variables
n0 = data['n']
h0 = data['h']
s0 = data['s']
c0 = data['c']
q0 = data['q']
z0 = data['z']
# baseline ion concentrations [mol/m**3]
cbK_se = 3.082
cbK_sg = 99.959
cbK_de = 3.082
cbK_dg = 99.959
cbCa_sn = 0.01
cbCa_dn = 0.01
# residual charges [mol]
res_sn = phi_msn * 3e-2 * 616e-12 / 9.648e4
res_sg = phi_msg * 3e-2 * 616e-12 / 9.648e4
res_se = res_sn + res_sg
res_dn = phi_mdn * 3e-2 * 616e-12 / 9.648e4
res_dg = phi_mdg * 3e-2 * 616e-12 / 9.648e4
res_de = res_dn + res_dg
X_sn = Na_sn0 + K_sn0 - Cl_sn0 + 2 * Ca_sn0 - res_sn
X_se = Na_se0 + K_se0 - Cl_se0 + 2 * Ca_se0 + res_se
X_sg = Na_sg0 + K_sg0 - Cl_sg0 - res_sg
X_dn = Na_dn0 + K_dn0 - Cl_dn0 + 2 * Ca_dn0 - res_dn
X_de = Na_de0 + K_de0 - Cl_de0 + 2 * Ca_de0 + res_de
X_dg = Na_dg0 + K_dg0 - Cl_dg0 - res_dg
# residual mass [mol/m**3]
cM_sn = (Na_sn0 + K_sn0 + Cl_sn0 + Ca_sn0) / V_sn0
cM_se = (Na_se0 + K_se0 + Cl_se0 + Ca_se0) / V_se0
cM_sg = (Na_sg0 + K_sg0 + Cl_sg0) / V_sg0
cM_dn = (Na_dn0 + K_dn0 + Cl_dn0 + Ca_dn0) / V_dn0
cM_de = (Na_de0 + K_de0 + Cl_de0 + Ca_de0) / V_de0
cM_dg = (Na_dg0 + K_dg0 + Cl_dg0) / V_dg0
# print initial values
# init_cell = edNEGmodel(T, Na_sn0, Na_se0, Na_sg0, Na_dn0, Na_de0, Na_dg0, K_sn0, K_se0, K_sg0, K_dn0, K_de0, K_dg0, Cl_sn0, Cl_se0, Cl_sg0, Cl_dn0, Cl_de0, Cl_dg0, Ca_sn0, Ca_se0, Ca_dn0, Ca_de0, X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se, cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n0, h0, s0, c0, q0, z0, V_sn0, V_se0, V_sg0, V_dn0, V_de0, V_dg0, cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
#print_initial_values(init_cell)
# define differential equations for different injection protocols
def dkdt_K_soma(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > stim_start and t < stim_end:
dKdt_sn += I_stim / my_cell.F
dKdt_se -= I_stim / my_cell.F
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
def dkdt_K_dendrite(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > stim_start and t < stim_end:
dKdt_dn += I_stim / my_cell.F
dKdt_de -= I_stim / my_cell.F
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
def dkdt_K_both(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > stim_start and t < stim_end:
dKdt_sn += I_stim / my_cell.F
dKdt_se -= I_stim / my_cell.F
dKdt_dn += I_stim / my_cell.F
dKdt_de -= I_stim / my_cell.F
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
def dkdt_Na_soma(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > stim_start and t < stim_end:
dNadt_sn += I_stim / my_cell.F
dNadt_se -= I_stim / my_cell.F
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
def dkdt_Na_dendrite(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > stim_start and t < stim_end:
dNadt_dn += I_stim / my_cell.F
dNadt_de -= I_stim / my_cell.F
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
def dkdt_Na_both(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > stim_start and t < stim_end:
dNadt_sn += I_stim / my_cell.F
dNadt_se -= I_stim / my_cell.F
dNadt_dn += I_stim / my_cell.F
dNadt_de -= I_stim / my_cell.F
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
def dkdt_Cl_soma(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > stim_start and t < stim_end:
dCldt_sn -= I_stim / my_cell.F
dCldt_se += I_stim / my_cell.F
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
def dkdt_Cl_dendrite(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > stim_start and t < stim_end:
dCldt_dn -= I_stim / my_cell.F
dCldt_de += I_stim / my_cell.F
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
def dkdt_Cl_both(t, k):
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = k
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn,
K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg,
Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de,
X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, cbK_se,
cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s,
c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg,
cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de = my_cell.dkdt(
)
dndt, dhdt, dsdt, dcdt, dqdt, dzdt = my_cell.dmdt()
dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt = my_cell.dVdt()
if t > stim_start and t < stim_end:
dCldt_sn -= I_stim / my_cell.F
dCldt_se += I_stim / my_cell.F
dCldt_dn -= I_stim / my_cell.F
dCldt_de += I_stim / my_cell.F
return dNadt_sn, dNadt_se, dNadt_sg, dNadt_dn, dNadt_de, dNadt_dg, dKdt_sn, dKdt_se, dKdt_sg, dKdt_dn, dKdt_de, dKdt_dg, \
dCldt_sn, dCldt_se, dCldt_sg, dCldt_dn, dCldt_de, dCldt_dg, dCadt_sn, dCadt_se, dCadt_dn, dCadt_de, \
dndt, dhdt, dsdt, dcdt, dqdt, dzdt, dVsidt, dVsedt, dVsgdt, dVdidt, dVdedt, dVdgdt
# solve
t_span = (0, t_dur)
k0 = [
Na_sn0, Na_se0, Na_sg0, Na_dn0, Na_de0, Na_dg0, K_sn0, K_se0, K_sg0,
K_dn0, K_de0, K_dg0, Cl_sn0, Cl_se0, Cl_sg0, Cl_dn0, Cl_de0, Cl_dg0,
Ca_sn0, Ca_se0, Ca_dn0, Ca_de0, n0, h0, s0, c0, q0, z0, V_sn0, V_se0,
V_sg0, V_dn0, V_de0, V_dg0
]
if injection_protocol == 'K_soma_ss':
sol = solve_ivp(dkdt_K_soma, t_span, k0, method='RK23', max_step=1e-5)
elif injection_protocol == 'K_soma_db':
sol = solve_ivp(dkdt_K_soma, t_span, k0, method='RK23', max_step=1e-4)
elif injection_protocol == 'K_dendrite_ss':
sol = solve_ivp(dkdt_K_dendrite,
t_span,
k0,
method='RK23',
max_step=1e-5)
elif injection_protocol == 'K_dendrite_db':
sol = solve_ivp(dkdt_K_dendrite,
t_span,
k0,
method='RK23',
max_step=1e-4)
elif injection_protocol == 'K_both_ss':
sol = solve_ivp(dkdt_K_both, t_span, k0, method='RK23', max_step=1e-5)
elif injection_protocol == 'K_both_db':
sol = solve_ivp(dkdt_K_both, t_span, k0, method='RK23', max_step=1e-4)
elif injection_protocol == 'Na_soma_ss':
sol = solve_ivp(dkdt_Na_soma, t_span, k0, method='RK23', max_step=1e-5)
elif injection_protocol == 'Na_soma_db':
sol = solve_ivp(dkdt_Na_soma, t_span, k0, method='RK23', max_step=1e-4)
elif injection_protocol == 'Na_dendrite_ss':
sol = solve_ivp(dkdt_Na_dendrite,
t_span,
k0,
method='RK23',
max_step=1e-5)
elif injection_protocol == 'Na_dendrite_db':
sol = solve_ivp(dkdt_Na_dendrite,
t_span,
k0,
method='RK23',
max_step=1e-4)
elif injection_protocol == 'Na_both_ss':
sol = solve_ivp(dkdt_Na_both, t_span, k0, method='RK23', max_step=1e-5)
elif injection_protocol == 'Na_both_db':
sol = solve_ivp(dkdt_Na_both, t_span, k0, method='RK23', max_step=1e-4)
elif injection_protocol == 'Cl_soma_ss':
sol = solve_ivp(dkdt_Cl_soma, t_span, k0, method='RK23', max_step=1e-5)
elif injection_protocol == 'Cl_soma_db':
sol = solve_ivp(dkdt_Cl_soma, t_span, k0, method='RK23', max_step=1e-4)
elif injection_protocol == 'Cl_dendrite_ss':
sol = solve_ivp(dkdt_Cl_dendrite,
t_span,
k0,
method='RK23',
max_step=1e-5)
elif injection_protocol == 'Cl_dendrite_db':
sol = solve_ivp(dkdt_Cl_dendrite,
t_span,
k0,
method='RK23',
max_step=1e-4)
elif injection_protocol == 'Cl_both_ss':
sol = solve_ivp(dkdt_Cl_both, t_span, k0, method='RK23', max_step=1e-5)
elif injection_protocol == 'Cl_both_db':
sol = solve_ivp(dkdt_Cl_both, t_span, k0, method='RK23', max_step=1e-4)
Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, Ca_sn, Ca_se, Ca_dn, Ca_de, n, h, s, c, q, z, V_sn, V_se, V_sg, V_dn, V_de, V_dg = sol.y
my_cell = edNEGmodel(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K_de, K_dg, Cl_sn, Cl_se, Cl_sg, Cl_dn, Cl_de, Cl_dg, \
Ca_sn, Ca_se, Ca_dn, Ca_de, X_sn, X_se, X_sg, X_dn, X_de, X_dg, alpha, \
cbK_se, cbK_sg, cbK_de, cbK_dg, cbCa_sn, cbCa_dn, n, h, s, c, q, z, \
V_sn, V_se, V_sg, V_dn, V_de, V_dg, cM_sn, cM_se, cM_sg, cM_dn, cM_de, cM_dg)
return sol, my_cell