brainpy.dyn.base.NeuGroup

class brainpy.dyn.base.NeuGroup(size, name=None)

Base class to model neuronal groups.

There are several essential attributes:

• size: the geometry of the neuron group. For example, (10, ) denotes a line of neurons, (10, 10) denotes a neuron group aligned in a 2D space, (10, 15, 4) denotes a 3-dimensional neuron group.

• num: the flattened number of neurons in the group. For example, size=(10, ) => num=10, size=(10, 10) => num=100, size=(10, 15, 4) => num=600.

Parameters

• size (int, tuple of int, list of int) – The neuron group geometry.

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

__init__(size, name=None)

Methods

__init__(size[, name])
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