brainpy.dyn.channels.Ca.ICa_p2q_markov#

class brainpy.dyn.channels.Ca.ICa_p2q_markov(size, keep_size=False, phi_p=3.0, phi_q=3.0, g_max=2.0, method='exp_auto', name=None, mode=NormalMode)[source]#

The calcium current model of \(p^2q\) current which described with first-order Markov chain.

The dynamics of this generalized calcium current model is given by:

\[\begin{split}I_{CaT} &= g_{max} p^2 q(V-E_{Ca}) \\ {dp \over dt} &= \phi_p (\alpha_p(V)(1-p) - \beta_p(V)p) \\ {dq \over dt} &= \phi_q (\alpha_q(V)(1-q) - \beta_q(V)q) \\\end{split}\]

where \(\phi_p\) and \(\phi_q\) are temperature-dependent factors, \(E_{Ca}\) is the reversal potential of Calcium channel.

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 maximum conductance.

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

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

__init__(size, keep_size=False, phi_p=3.0, phi_q=3.0, g_max=2.0, method='exp_auto', name=None, mode=NormalMode)[source]#

Methods

__init__(size[, keep_size, phi_p, phi_q, ...])

clear_input()

current(V, C_Ca, E_Ca)

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, C_Ca, E_Ca[, batch_size])

Reset function which reset the whole variables in the model.

reset_local_delays([nodes])

Reset local delay variables.

reset_state(V, C_Ca, E_Ca[, 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, C_Ca, E_Ca)

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