brainpy.channels.IKA1_HM1992#

class brainpy.channels.IKA1_HM1992(size, keep_size=False, E=-90.0, g_max=30.0, V_sh=0.0, phi_p=1.0, phi_q=1.0, method='exp_auto', name=None, mode=None)[source]#

The rapidly inactivating Potassium channel (IA1) model proposed by (Huguenard & McCormick, 1992) [2].

This model is developed according to the average behavior of rapidly inactivating Potassium channel in Thalamus relay neurons [2] [1].

\[\begin{split}&IA = g_{\mathrm{max}} p^4 q (E-V) \\ &\frac{dp}{dt} = \phi_p \frac{p_{\infty} - p}{\tau_p} \\ &p_{\infty} = \frac{1}{1+ \exp[-(V -V_{sh}+ 60)/8.5]} \\ &\tau_{p}=\frac{1}{\exp \left(\frac{V -V_{sh}+35.8}{19.7}\right)+ \exp \left(\frac{V -V_{sh}+79.7}{-12.7}\right)}+0.37 \\ &\frac{dq}{dt} = \phi_q \frac{q_{\infty} - q}{\tau_q} \\ &q_{\infty} = \frac{1}{1+ \exp[(V -V_{sh} + 78)/6]} \\ &\begin{array}{l} \tau_{q} = \frac{1}{\exp((V -V_{sh}+46)/5.) + \exp((V -V_{sh}+238)/-37.5)} \quad V<(-63+V_{sh})\, mV \\ \tau_{q} = 19 \quad V \geq (-63 + V_{sh})\, mV \end{array}\end{split}\]

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

See also

IKA2_HM1992

__init__(size, keep_size=False, E=-90.0, g_max=30.0, 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, compatible])

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.

register_delay(identifier, delay_step, ...)

Register delay variable.

register_implicit_nodes(*nodes[, node_cls])

register_implicit_vars(*variables[, var_cls])

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)

Unflatten the data to construct an object of this class.

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