hodgkin-huxley.toml

Source: src/dynlib/models/ode/hodgkin-huxley.toml

#! dynlib
[model]
type = "ode"
name = "Hodgkin-Huxley Neuron (activation-function form)"
dtype = "float64"

[states]
# Resting equilibrium for I = 0 (from old resting_ic())
V = -65.0
n = 0.31
m = 0.06
h = 0.61

[params]
# Membrane / channels
C   = 1.0
EK  = -77.0
ENa = 55.0
EL  = -54.0
gK  = 36.0
gNa = 120.0
gL  = 0.3
I   = 0.0

# Na activation (m)
Vmid_m  = -40.0
k_m     = 9.0
Vmax_m  = -38.0
sigma_m = 30.0
Camp_m  = 0.46
Cbase_m = 0.04

# Na inactivation (h)
Vmid_h  = -62.0
k_h     = -7.0
Vmax_h  = -67.0
sigma_h = 20.0
Camp_h  = 7.4
Cbase_h = 1.2

# K activation (n)
Vmid_n  = -53.0
k_n     = 15.0
Vmax_n  = -79.0
sigma_n = 50.0
Camp_n  = 4.7
Cbase_n = 1.1

[functions.boltzmann]
args = ["Vmid", "k", "V"]
expr = "1.0 / (1.0 + exp((Vmid - V) / k))"

[functions.gaussian]
args = ["Vmax", "sigma", "Camp", "Cbase", "V"]
expr = "Cbase + Camp * exp(-((Vmax - V)**2) / (sigma**2))"

[aux]
# Steady-state activation / inactivation
m_inf = "boltzmann(Vmid_m, k_m, V)"
h_inf = "boltzmann(Vmid_h, k_h, V)"
n_inf = "boltzmann(Vmid_n, k_n, V)"

# Time constants
tau_m = "gaussian(Vmax_m, sigma_m, Camp_m, Cbase_m, V)"
tau_h = "gaussian(Vmax_h, sigma_h, Camp_h, Cbase_h, V)"
tau_n = "gaussian(Vmax_n, sigma_n, Camp_n, Cbase_n, V)"

[equations.rhs]
V = "(I - gK*n**4*(V - EK) - gNa*m**3*h*(V - ENa) - gL*(V - EL)) / C"
n = "(n_inf - n) / tau_n"
m = "(m_inf - m) / tau_m"
h = "(h_inf - h) / tau_h"