brainpy.dyn.channels.CalciumAbstract

class brainpy.dyn.channels.CalciumAbstract(size, alpha=0.13, beta=0.075, C_initializer=OneInit(value=0.05), E_initializer=OneInit(value=120.0), method='exp_auto', name=None)

The first-order calcium concentration model.

\[Ca' = -\alpha I_{Ca} + -\beta Ca\]

__init__(size, alpha=0.13, beta=0.075, C_initializer=OneInit(value=0.05), E_initializer=OneInit(value=120.0), method='exp_auto', name=None)

Methods

__init__(size[, alpha, beta, C_initializer, ...])
current(V[, C_Ca, E_Ca])
derivative(C, t, V)
get_delay_data(name, delay_step, *indices)	Get delay data according to the provided delay steps.
has(**children_cls)	The aggressive operation to gather master and children classes.
ints([method])	Collect all integrators in this node and the children nodes.
load_states(filename[, verbose])	Load the model states.
nodes([method, level, include_self])	Collect all children nodes.
register_delay(name, delay_step, delay_target)	Register delay variable.
register_implicit_nodes(nodes)
register_implicit_vars(variables)
reset(V[, C_Ca, E_Ca])	Reset function which reset the whole variables in the model.
reset_delay(name, delay_target)	Reset the delay variable.
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(t, dt, V)	Step function of a network.
update_delay(name, delay_data)	Update the delay according to the delay data.
vars([method, level, include_self])	Collect all variables in this node and the children nodes.

Attributes

global_delay_vars
name
steps