superneuromat.SNN.ispikes

property SNN.ispikes: ndarray[(<class 'int'>, <class 'int'>), None]

Convert the output spike train to a dense binary numpy.ndarray.

This is useful for slicing and analyzing the spike train.

Index with snn.ispikes[time, neuron].

Note that this is converted from the output spike train, which may be cleared with clear_spike_train() or reset().

Returns:

The dtype of the array will be default_bool_dtype.

Return type:

numpy.ndarray[(int, int), bool]

Examples

>>> # Here's an example spike train:
>>> snn.ispikes
array([[False, False, False, False,  True],
       [False,  True, False,  True,  True],
       [ True,  True,  True,  True,  True],
       [ True,  True, False, False, False]])
>>> snn.print_spike_train()
t|id0 1 2 3 4
0: [│ │ │ │ ├─]
1: [│ ├─│ ├─├─]
2: [├─├─├─├─├─]
3: [├─├─│ │ │ ]

>>> # single neuron
>>> snn.ispikes[0, 0]  # True if neuron 0 spiked at time 0
np.False_

>>> # multiple neurons
>>> snn.ispikes[0, :]  # Whether a neuron spiked at time step 0
array([False, False, False, False,  True])
>>> snn.ispikes[:, 0]  # Whether neuron 0 spiked for a particular time step
array([False, False,  True,  True])
>>> snn.ispikes[-1]  # Whether a neuron spiked at the last time step
array([ True,  True, False, False, False])
>>> snn.ispikes[-3:].sum()  # Number of spikes emitted in the last 3 time steps
np.int64(10)
>>> snn.ispikes[-3:].sum(0)  # Number of spikes emitted for each neuron in the last 3 time steps
array([2, 3, 1, 2, 2])
>>> snn.ispikes[:, 2:].sum(0)  # Number of spikes emitted by the first 2 neurons over all time
array([2, 3])
>>> snn.ispikes.sum(1)  # number of spikes emitted per neuron over all time
array([1, 3, 5, 2])