brainpy.neurons.SpikeTimeGroup#

class brainpy.neurons.SpikeTimeGroup(size, times, indices, need_sort=True, keep_size=False, mode=None, 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, ArrayType) – The neuron indices at each time point to emit spikes.
times (list, tuple, ArrayType) – 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=None, name=None)[source]#

Methods

`__init__`(size, times, indices[, need_sort, ...])
`clear_input`()	Function to clear inputs in the neuron group.
`cpu`()	Move all variable into the CPU device.
`cuda`()	Move all variables into the GPU device.
`get_batch_shape`([batch_size])
`get_delay_data`(identifier, delay_step, *indices)	Get delay data according to the provided delay steps.
`load_state_dict`(state_dict[, warn])	Copy parameters and buffers from `state_dict` into this module and its descendants.
`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[, node_cls])
`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.
`state_dict`()	Returns a dictionary containing a whole state of the module.
`to`(device)	Moves all variables into the given device.
`tpu`()	Move all variables into the TPU device.
`train_vars`([method, level, include_self])	The shortcut for retrieving all trainable variables.
`tree_flatten`()	Flattens the object as a PyTree.
`tree_unflatten`(aux, dynamic_values)	New in version 2.3.1.
`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`	Global delay data, which stores the delay variables and corresponding delay targets.
`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.