brainpy.dyn.neurons.SpikeTimeGroup#

class brainpy.dyn.neurons.SpikeTimeGroup(size, times, indices, need_sort=True, keep_size=False, mode=NormalMode, 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
  • size (int, tuple, list) – The neuron group geometry.

  • indices (list, tuple, np.ndarray, JaxArray, jax.numpy.ndarray) – The neuron indices at each time point to emit spikes.

  • times (list, tuple, np.ndarray, JaxArray, jax.numpy.ndarray) – The time points which generate the spikes.

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

__init__(size, times, indices, need_sort=True, keep_size=False, mode=NormalMode, name=None)[source]#

Methods

__init__(size, times, indices[, need_sort, ...])

clear_input()

Function to clear inputs in the neuron group.

get_batch_shape([batch_size])

get_delay_data(identifier, delay_step, *indices)

Get delay data according to the provided delay steps.

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, **named_nodes)

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.

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(tdi[, x])

The function to specify the updating rule.

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

mode

Mode of the model, which is useful to control the multiple behaviors of the model.

name

Name of the model.

varshape

The shape of variables in the neuron group.