Hodgkin-Huxley Model

A.L. Hodgkin, A.F. Huxley, "A quantitative description of membrane current and its application to conduction and excitation in nerve", J. Physiol., 117, 500-544, 1952.



In [1]:

    
%matplotlib inline
import numpy as np
from ecell4 import *



In [2]:

    
Q10 = 3.0
GNa = 120.0 # mS/cm^2
GK = 36.0 # mS/cm^2
gL = 0.3 # mS/cm^2
EL = -64.387 # mV
ENa = 40.0 # mV
EK = -87.0 # mV
Cm = 1.0 # uF/cm^2

T = 6.3 # degrees C
Iext = 10.0 # nA

with reaction_rules():
    Q = Q10 ** ((T - 6.3) / 10)

    alpha_m = -0.1 * (Vm + 50) / (exp(-(Vm + 50) / 10) - 1)
    beta_m = 4 * exp(-(Vm + 75) / 18)
    ~m > m | Q * (alpha_m * (1 - m) - beta_m * m)

    alpha_h = 0.07 * exp(-(Vm + 75) / 20)
    beta_h = 1.0 / (exp(-(Vm + 45) / 10) + 1)
    ~h > h | Q * (alpha_h * (1 - h) - beta_h * h)

    alpha_n = -0.01 * (Vm + 65) / (exp(-(Vm + 65) / 10) - 1)
    beta_n = 0.125 * exp(-(Vm + 75) / 80)    
    ~n > n | Q * (alpha_n * (1 - n) - beta_n * n)

    gNa = (m ** 3) * h * GNa
    INa = gNa * (Vm - ENa)
    gK = (n ** 4) * GK
    IK = gK * (Vm - EK)
    IL = gL * (Vm - EL)
    ~Vm > Vm | (Iext - (IL + INa + IK)) / Cm

hhm = get_model()



In [3]:

    
for rr in hhm.reaction_rules():
    print(rr.as_string())









    



1*Vm>1*m+1*Vm|0
1*Vm>1*h+1*Vm|0
1*Vm>1*n+1*Vm|0
1*m+1*h+1*n>1*Vm+1*m+1*h+1*n|0



In [4]:

    
run_simulation(np.linspace(0, 100, 1001), model=hhm, y0={'Vm': -75}, species_list=['Vm'])

FitzHugh–Nagumo Model

R. FitzHugh, "Mathematical models of threshold phenomena in the nerve membrane.", Bull. Math. Biophysics, 17:257—278, 1955.



In [5]:

    
a = 0.7
b = 0.8
c = 12.5
Iext = 0.5

with reaction_rules():
    ~u > u | -v + u - (u ** 3) / 3 + Iext
    ~v > v | (u - b * v + a) / c

fnm = get_model()



In [6]:

    
for rr in fnm.reaction_rules():
    print(rr.as_string())









    



1*v>1*u+1*v|0
1*u>1*v+1*u|0



In [7]:

    
run_simulation(np.linspace(0, 200, 501), model=fnm)



In [8]:

    
run_simulation(np.linspace(0, 200, 501), model=fnm, # return_type='nyaplot',
               opt_kwargs={'x': 'u', 'y': ['v']})