brainpy.dyn.channels.Ca.CalciumFixed#

class brainpy.dyn.channels.Ca.CalciumFixed(size, keep_size=False, E=120.0, C=0.00024, method='exp_auto', name=None, mode=NormalMode, **channels)[source]#

Fixed Calcium dynamics.

This calcium model has no dynamics. It holds fixed reversal potential \(E\) and concentration \(C\).

__init__(size, keep_size=False, E=120.0, C=0.00024, method='exp_auto', name=None, mode=NormalMode, **channels)[source]#

Methods

__init__(size[, keep_size, E, C, method, ...])

clear_input()

current(V[, C_Ca, E_Ca])

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(*channels, ...)

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[, 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)

Update function of a container.

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