brainpy.dyn.channels.K.IKA_p4q_ss#

class brainpy.dyn.channels.K.IKA_p4q_ss(size, keep_size=False, E=- 90.0, g_max=10.0, phi_p=1.0, phi_q=1.0, method='exp_auto', name=None, mode=NormalMode)[source]#

The rapidly inactivating Potassium channel of \(p^4q\) current which described with steady-state format.

This model is developed according to the average behavior of rapidly inactivating Potassium channel in Thalamus relay neurons 2 3.

\[\begin{split}&IA = g_{\mathrm{max}} p^4 q (E-V) \\ &\frac{dp}{dt} = \phi_p \frac{p_{\infty} - p}{\tau_p} \\ &\frac{dq}{dt} = \phi_q \frac{q_{\infty} - q}{\tau_q} \\\end{split}\]

where \(\phi_p\) and \(\phi_q\) are the temperature dependent factors (default 1.).

Parameters
  • size (int, sequence of int) – The geometry size.

  • method (str) – The numerical integration method.

  • name (str) – The object name.

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

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

  • phi_p (optional, float, Array, Callable, Initializer) – The temperature factor for channel \(p\).

  • phi_q (optional, float, Array, Callable, Initializer) – The temperature factor for channel \(q\).

References

2

Huguenard, John R., and David A. McCormick. “Simulation of the currents involved in rhythmic oscillations in thalamic relay neurons.” Journal of neurophysiology 68.4 (1992): 1373-1383.

3

Huguenard, J. R., and D. A. Prince. “Slow inactivation of a TEA-sensitive K current in acutely isolated rat thalamic relay neurons.” Journal of neurophysiology 66.4 (1991): 1316-1328.

__init__(size, keep_size=False, E=- 90.0, g_max=10.0, phi_p=1.0, phi_q=1.0, method='exp_auto', name=None, mode=NormalMode)[source]#

Methods

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

clear_input()

current(V)

dp(p, t, V)

dq(q, t, V)

f_p_inf(V)

f_p_tau(V)

f_q_inf(V)

f_q_tau(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