brainpy.dyn.base.DynamicalSystem#

class brainpy.dyn.base.DynamicalSystem(name=None, mode=None)[source]#

Base Dynamical System class.

Parameters
  • name (optional, str) – The name of the dynamical system.

  • mode (Mode) – The model computation mode. It should be instance of Mode.

__init__(name=None, mode=None)[source]#

Methods

__init__([name, mode])

clear_input()

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(*nodes, **named_nodes)

register_implicit_vars(*variables, ...)

reset([batch_size])

Reset function which reset the whole variables in the model.

reset_local_delays([nodes])

Reset local delay variables.

reset_state([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(*args, **kwargs)

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

mode

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

name

Name of the model.

online_fit_by

Offline fitting method.

offline_fit_by

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