brainpy.dyn.channels.Na.INa_TM1991#

class brainpy.dyn.channels.Na.INa_TM1991(size, keep_size=False, E=50.0, g_max=120.0, phi=1.0, V_sh=- 63.0, method='exp_auto', name=None, mode=NormalMode)[source]#

The sodium current model described by (Traub and Miles, 1991) 1.

The dynamics of this sodium current model is given by:

\[\begin{split}\begin{split} \begin{aligned} I_{\mathrm{Na}} &= g_{\mathrm{max}} m^3 h \\ \frac {dm} {dt} &= \phi(\alpha_m (1-x) - \beta_m) \\ &\alpha_m(V) = 0.32 \frac{(13 - V + V_{sh})}{\exp((13 - V +V_{sh}) / 4) - 1.} \\ &\beta_m(V) = 0.28 \frac{(V - V_{sh} - 40)}{(\exp((V - V_{sh} - 40) / 5) - 1)} \\ \frac {dh} {dt} &= \phi(\alpha_h (1-x) - \beta_h) \\ &\alpha_h(V) = 0.128 * \exp((17 - V + V_{sh}) / 18) \\ &\beta_h(V) = 4. / (1 + \exp(-(V - V_{sh} - 40) / 5)) \\ \end{aligned} \end{split}\end{split}\]

where \(V_{sh}\) is the membrane shift (default -63 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

Traub, Roger D., and Richard Miles. Neuronal networks of the hippocampus. Vol. 777. Cambridge University Press, 1991.

See also

INa_Ba2002

__init__(size, keep_size=False, E=50.0, g_max=120.0, phi=1.0, V_sh=- 63.0, 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