brainpy.channels.IKK2A_HM1992#

class brainpy.channels.IKK2A_HM1992(size, keep_size=False, E=- 90.0, g_max=10.0, V_sh=0.0, phi_p=1.0, phi_q=1.0, method='exp_auto', name=None, mode=None)[source]#

The slowly inactivating Potassium channel (IK2a) model proposed by (Huguenard & McCormick, 1992) 2.

The dynamics of the model is given as 2 3.

\[\begin{split}&IK2 = g_{\mathrm{max}} p q (E-V) \\ &\frac{dp}{dt} = \phi_p \frac{p_{\infty} - p}{\tau_p} \\ &p_{\infty} = \frac{1}{1+ \exp[-(V -V_{sh}+ 43)/17]} \\ &\tau_{p}=\frac{1}{\exp \left(\frac{V -V_{sh}-81.}{25.6}\right)+ \exp \left(\frac{V -V_{sh}+132}{-18}\right)}+9.9 \\ &\frac{dq}{dt} = \phi_q \frac{q_{\infty} - q}{\tau_q} \\ &q_{\infty} = \frac{1}{1+ \exp[(V -V_{sh} + 59)/10.6]} \\ & \tau_{q} = \frac{1}{\exp((V -V_{sh}+1329)/200.) + \exp((V -V_{sh}+130)/-7.1)} + 120 \\\end{split}\]

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, ArrayType, Initializer, Callable) – The maximal conductance density (\(mS/cm^2\)).

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

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

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

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

References

2(1,2)

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, V_sh=0.0, phi_p=1.0, phi_q=1.0, method='exp_auto', name=None, mode=None)[source]#

Methods

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

clear_input()

cpu()

Move all variable into the CPU device.

cuda()

Move all variables into the GPU device.

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_state_dict(state_dict[, warn])

Copy parameters and buffers from state_dict into this module and its descendants.

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[, node_cls])

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.

state_dict()

Returns a dictionary containing a whole state of the module.

to(device)

Moves all variables into the given device.

tpu()

Move all variables into the TPU device.

train_vars([method, level, include_self])

The shortcut for retrieving all trainable variables.

tree_flatten()

Flattens the object as a PyTree.

tree_unflatten(aux, dynamic_values)

New in version 2.3.1.

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

Global delay data, which stores the delay variables and corresponding delay targets.

mode

Mode of the model, which is useful to control the multiple behaviors of the model.

name

Name of the model.

varshape