brainpy.dyn.channels.Na.INa_p3q_markov#

class brainpy.dyn.channels.Na.INa_p3q_markov(size, keep_size=False, E=50.0, g_max=90.0, phi=1.0, method='exp_auto', name=None, mode=NormalMode)[source]#

The sodium current model of \(p^3q\) current which described with first-order Markov chain.

The general model can be used to model the dynamics with:

\[\begin{split}\begin{aligned} I_{\mathrm{Na}} &= g_{\mathrm{max}} * p^3 * q \\ \frac{dp}{dt} &= \phi ( \alpha_p (1-p) - \beta_p p) \\ \frac{dq}{dt} & = \phi ( \alpha_q (1-h) - \beta_q h) \\ \end{aligned}\end{split}\]

where \(\phi\) is a temperature-dependent factor.

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

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

  • phi (float, Array, Callable, Initializer) – The temperature-dependent factor.

  • method (str) – The numerical method

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

__init__(size, keep_size=False, E=50.0, g_max=90.0, phi=1.0, 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