brainpy.dyn.channels.Na.INa_HH1952#

class brainpy.dyn.channels.Na.INa_HH1952(size, keep_size=False, E=50.0, g_max=120.0, phi=1.0, V_sh=- 45.0, method='exp_auto', name=None, mode=NormalMode)[source]#

The sodium current model described by Hodgkin–Huxley model 1.

The dynamics of this sodium current model is given by:

\[\begin{split}\begin{split} \begin{aligned} I_{\mathrm{Na}} &= g_{\mathrm{max}} m^3 h \\ \frac {dm} {dt} &= \phi (\alpha_m (1-x) - \beta_m) \\ &\alpha_m(V) = \frac {0.1(V-V_{sh}-5)}{1-\exp(\frac{-(V -V_{sh} -5)} {10})} \\ &\beta_m(V) = 4.0 \exp(\frac{-(V -V_{sh}+ 20)} {18}) \\ \frac {dh} {dt} &= \phi (\alpha_h (1-x) - \beta_h) \\ &\alpha_h(V) = 0.07 \exp(\frac{-(V-V_{sh}+20)}{20}) \\ &\beta_h(V) = \frac 1 {1 + \exp(\frac{-(V -V_{sh}-10)} {10})} \\ \end{aligned} \end{split}\end{split}\]

where \(V_{sh}\) is the membrane shift (default -45 mV), and \(\phi\) is the temperature-dependent factor (default 1.).

Parameters
  • size (int, tuple of int) – The size of the simulation target.

  • keep_size (bool) – Keep size or flatten the size?

  • method (str) – The numerical method

  • name (str) – The name of the object.

  • g_max (float, Array, Callable, Initializer) – The maximal conductance density (\(mS/cm^2\)).

  • E (float, Array, Callable, Initializer) – The reversal potential (mV).

  • V_sh (float, Array, Callable, Initializer) – The membrane shift.

References

1

Hodgkin, Alan L., and Andrew F. Huxley. “A quantitative description of membrane current and its application to conduction and excitation in nerve.” The Journal of physiology 117.4 (1952): 500.

See also

IK_HH1952

__init__(size, keep_size=False, E=50.0, g_max=120.0, phi=1.0, V_sh=- 45.0, method='exp_auto', name=None, mode=NormalMode)[source]#

Methods

__init__(size[, keep_size, E, g_max, phi, ...])

clear_input()

current(V)

dp(p, t, V)

dq(q, t, V)

f_p_alpha(V)

f_p_beta(V)

f_q_alpha(V)

f_q_beta(V)

get_delay_data(identifier, delay_step, *indices)

Get delay data according to the provided delay steps.

load_states(filename[, verbose])

Load the model states.

nodes([method, level, include_self])

Collect all children nodes.

offline_fit(target, fit_record)

offline_init()

online_fit(target, fit_record)

online_init()

register_delay(identifier, delay_step, ...)

Register delay variable.

register_implicit_nodes(*nodes, **named_nodes)

register_implicit_vars(*variables, ...)

reset(V[, batch_size])

Reset function which reset the whole variables in the model.

reset_local_delays([nodes])

Reset local delay variables.

reset_state(V[, batch_size])

Reset function which reset the states in the model.

save_states(filename[, variables])

Save the model states.

train_vars([method, level, include_self])

The shortcut for retrieving all trainable variables.

unique_name([name, type_])

Get the unique name for this object.

update(tdi, V)

The function to specify the updating rule.

update_local_delays([nodes])

Update local delay variables.

vars([method, level, include_self])

Collect all variables in this node and the children nodes.

Attributes

global_delay_data

mode

Mode of the model, which is useful to control the multiple behaviors of the model.

name

Name of the model.

varshape