brainpy.dyn.rates.DelayCoupling

class brainpy.dyn.rates.DelayCoupling(delay_var, target_var, conn_mat, required_shape, delay_steps=None, initial_delay_data=None, name=None)

Delay coupling.

Parameters

• delay_var (Variable) – The delay variable.

• target_var (Variable, sequence of Variable) – The target variables to output.

• conn_mat (JaxArray, ndarray) – The connection matrix.

• required_shape (sequence of int) – The required shape of (pre, post).

• delay_steps (int, JaxArray, ndarray) – The matrix of delay time steps. Must be int.

• initial_delay_data (Initializer, Callable) – The initializer of the initial delay data.

__init__(delay_var, target_var, conn_mat, required_shape, delay_steps=None, initial_delay_data=None, name=None)

Methods

__init__(delay_var, target_var, conn_mat, ...)
get_delay_data(name, delay_step, *indices)	Get delay data according to the provided delay steps.
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()	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)	The function to specify the updating rule.
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