brainpy.dyn.others.SpikeTimeGroup
brainpy.dyn.others.SpikeTimeGroup#
- class brainpy.dyn.others.SpikeTimeGroup(size, times, indices, need_sort=True, keep_size=False, name=None)[source]#
The input neuron group characterized by spikes emitting at given times.
>>> # Get 2 neurons, firing spikes at 10 ms and 20 ms. >>> SpikeTimeGroup(2, times=[10, 20]) >>> # or >>> # Get 2 neurons, the neuron 0 fires spikes at 10 ms and 20 ms. >>> SpikeTimeGroup(2, times=[10, 20], indices=[0, 0]) >>> # or >>> # Get 2 neurons, neuron 0 fires at 10 ms and 30 ms, neuron 1 fires at 20 ms. >>> SpikeTimeGroup(2, times=[10, 20, 30], indices=[0, 1, 0]) >>> # or >>> # Get 2 neurons; at 10 ms, neuron 0 fires; at 20 ms, neuron 0 and 1 fire; >>> # at 30 ms, neuron 1 fires. >>> SpikeTimeGroup(2, times=[10, 20, 20, 30], indices=[0, 0, 1, 1])
- Parameters
Methods
__init__
(size, times, indices[, need_sort, ...])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