brainpy.dyn.channels.K.IKNI_Ya1989#

class brainpy.dyn.channels.K.IKNI_Ya1989(size, keep_size=False, E=- 90.0, g_max=0.004, phi_p=1.0, phi_q=1.0, tau_max=4000.0, V_sh=0.0, method='exp_auto', name=None, mode=NormalMode)[source]#

A slow non-inactivating K+ current described by Yamada et al. (1989) 1.

This slow potassium current can effectively account for spike-frequency adaptation.

\[\begin{split}\begin{aligned} &I_{M}=\bar{g}_{M} p\left(V-E_{K}\right) \\ &\frac{\mathrm{d} p}{\mathrm{~d} t}=\left(p_{\infty}(V)-p\right) / \tau_{p}(V) \\ &p_{\infty}(V)=\frac{1}{1+\exp [-(V-V_{sh}+35) / 10]} \\ &\tau_{p}(V)=\frac{\tau_{\max }}{3.3 \exp [(V-V_{sh}+35) / 20]+\exp [-(V-V_{sh}+35) / 20]} \end{aligned}\end{split}\]

where \(\bar{g}_{M}\) was \(0.004 \mathrm{mS} / \mathrm{cm}^{2}\) and \(\tau_{\max }=4 \mathrm{~s}\), unless stated otherwise.

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).

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

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

  • tau_max (float, Array, Callable, Initializer) – The \(tau_{\max}\) parameter.

References

1

Yamada, Walter M. “Multiple channels and calcium dynamics.” Methods in neuronal modeling (1989): 97-133.

__init__(size, keep_size=False, E=- 90.0, g_max=0.004, phi_p=1.0, phi_q=1.0, tau_max=4000.0, V_sh=0.0, 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)

f_p_inf(V)

f_p_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