brainpy.Network

class brainpy.Network(*ds_tuple, name=None, mode=None, **ds_dict)

Base class to model network objects, an alias of Container.

Network instantiates a network, which is aimed to load neurons, synapses, and other brain objects.

Parameters

• name (str, Optional) – The network name.

• monitors (optional, list of str, tuple of str) – The items to monitor.

• ds_tuple – A list/tuple container of dynamical system.

• ds_dict – A dict container of dynamical system.

__init__(*ds_tuple, name=None, mode=None, **ds_dict)

Methods

__init__(*ds_tuple[, name, mode])
clear_input()	Clear inputs in the children classes.
cpu()	Move all variable into the CPU device.
cuda()	Move all variables into the GPU device.
get_delay_data(identifier, delay_step, *indices)	Get delay data according to the provided delay steps.
load_state_dict(state_dict[, warn])	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.
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[, node_cls])
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.
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)	New in version 2.3.1.
unique_name([name, type_])	Get the unique name for this object.
update(*args, **kwargs)	Step function of a network.
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.