brainpy.dyn.base.ConstantDelay
brainpy.dyn.base.ConstantDelay#
- class brainpy.dyn.base.ConstantDelay(size, delay, dtype=None, dt=None, **kwargs)[source]#
Class used to model constant delay variables.
This class automatically supports batch size on the last axis. For example, if you run batch with the size of (10, 100), where 100 are batch size, then this class can automatically support your batched data. For examples,
>>> import brainpy as bp >>> bp.dyn.ConstantDelay(size=(10, 100), delay=10.)
This class also support nonuniform delays.
>>> bp.dyn.ConstantDelay(size=100, delay=bp.math.random.random(100) * 4 + 10)
- Parameters
Methods
__init__
(size, delay[, dtype, dt])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.
pull
()push
(value)register_delay
(name, delay_step, delay_target)Register delay variable.
register_implicit_nodes
(nodes)register_implicit_vars
(variables)reset
()Reset the variables.
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])Update the delay index.
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
latest
name
oldest
steps