brainpy.simulation.brainobjects.SpikeTimeInput

class brainpy.simulation.brainobjects.SpikeTimeInput(size, times, indices, need_sort=True, **kwargs)

The input neuron group characterized by spikes emitting at given times.

>>> # Get 2 neurons, firing spikes at 10 ms and 20 ms.
>>> SpikeTimeInput(2, times=[10, 20])
>>> # or
>>> # Get 2 neurons, the neuron 0 fires spikes at 10 ms and 20 ms.
>>> SpikeTimeInput(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.
>>> SpikeTimeInput(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.
>>> SpikeTimeInput(2, times=[10, 20, 20, 30], indices=[0, 0, 1, 1])

Parameters

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

• indices (int, list, tuple) – The neuron indices at each time point to emit spikes.

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

• steps (tuple of str, tuple of function, dict of (str, function), optional) – The callable function, or a list of callable functions.

• monitors (None, list, tuple, datastructures.Monitor) – Variables to monitor.

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

__init__(size, times, indices, need_sort=True, **kwargs)

Methods

__init__(size, times, indices[, need_sort])
build([inputs, method, show_code])
cpu()
cuda()
ints([method])	Collect all integrators in this node and the children nodes.
load_states(filename[, verbose, check])	Load the model states.
nodes([method, _paths])	Collect all children nodes.
register_constant_delay(key, size, delay[, ...])	Register a constant delay.
run(duration[, dt, report, inputs, extra_func])	The running function.
save_states(filename[, all_vars])	Save the model states.
struct_run(duration[, dt])
to(devices)
tpu()
train_vars([method])	The shortcut for retrieving all trainable variables.
unique_name([name, type])	Get the unique name for this object.
update(_t, _i, **kwargs)	The function to specify the updating rule.
vars([method])	Collect all variables in this node and the children nodes.

Attributes

implicit_nodes	Used to wrap the implicit children nodes which cannot be accessed by self.xxx
implicit_vars	Used to wrap the implicit variables which cannot be accessed by self.xxx
target_backend	Used to specify the target backend which the model to run.