brainpy.dyn.base.Network

class brainpy.dyn.base.Network(*ds_tuple, name=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, **ds_dict)

Methods

__init__(*ds_tuple[, name])
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()	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)	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