superneuromat.Neuron#

class superneuromat.Neuron(snn=<object object>, idx: int = <object object>, *args, **kwargs)[source]#

Accessor Class for Neurons in SNNs

Warning

Instances of Neurons are cached at access time as of v3.4.0. i.e. snn.neurons[0] is snn.neurons[0]. Prior to v3.4.0, new Neuron instances were created on each access.

Attributes:

child_ids: Returns a list of the IDs of the child neurons of this neuron.
children: Returns a list of the child neurons of this neuron.
incoming_synapses: Returns a list of the synapses that have this neuron as their post-synaptic neuron.
incoming_synaptic_ids: Returns a list of the synaptic ids of the synapses that have this neuron as their post-synaptic neuron.
leak: The amount by which the internal state of this neuron is pushed towards its reset state.
m: The SNN that this object is associated with.
num_onmodel: The number of neurons in the SNN.
outgoing_synapses: Returns a list of the synapses that have this neuron as their pre-synaptic neuron.
outgoing_synaptic_ids: Returns a list of the synaptic ids of the synapses that have this neuron as their pre-synaptic neuron.
parent_ids: Returns a list of the IDs of the parent neurons of this neuron.
parents: Returns a list of the parent neurons of this neuron.
refractory_period: The number of time steps for which this neuron should be in its refractory period.
refractory_state: The remaining number of time steps for which this neuron is in its refractory period.
reset_state: The charge state of this neuron immediately after spiking.
spikes: A vector of the spikes that have been emitted by this neuron.
state: The charge state of this neuron.
threshold: The > threshold value for this neuron to spike.

Methods

`add_spike`(time[, value])	Queue a spike to be sent to this Neuron.
`add_spikes`(spikes[, time_offset, exist])	Add a time-series of spikes to this neuron.
`connect_child`(child[, weight, delay, ...])	Connect this neuron to a child neuron.
`connect_parent`(parent[, weight, delay, ...])	Connect this neuron to a parent neuron.
`get_synapse_from`(neuron)	Returns the synapse connecting the given neuron to this neuron (directional).
`get_synapse_to`(neuron)	Returns the synapse connecting this neuron to the given neuron (directional).
`get_synaptic_id_from`(neuron)	Returns the synaptic id of the synapse connecting the given neuron to this neuron (directional).
`get_synaptic_id_to`(neuron)	Returns the synaptic id of the synapse connecting this neuron to the given neuron (directional).
`info`()	Returns a string containing information about this neuron.
`info_row`()	Returns a string containing information about this neuron for use in a table.
`spikes_str`([max_steps, use_unicode])	Returns a pretty string of the spikes that have been emitted by this neuron.

row_cont
row_header

add_spike(time: int, value: float = 1.0, **kwargs)[source]#

Queue a spike to be sent to this Neuron.

Parameters:

time (int) – The number of time_steps until the spike is sent.
value (float, default=1.0) – The value of the spike.
exist (str, default='error') – Action if a queued spike already exists at the given time step. Should be one of [‘error’, ‘overwrite’, ‘add’, ‘dontadd’].

add_spikes(spikes: list[float] | list[tuple[int, float]] | ~numpy.ndarray[(<class 'int'>,), ~numpy.dtype] | ~numpy.ndarray[(<class 'int'>, 2), ~numpy.dtype], time_offset: int = 0, exist: str = 'error')[source]#

Add a time-series of spikes to this neuron.

Parameters:

spikes (numpy.typing.ArrayLike)
time_offset (int, default=0) – The number of time steps to offset the spikes by.
exist (str, default='error') – Action if a queued spike already exists at the given time step. Should be one of [‘error’, ‘overwrite’, ‘add’, ‘dontadd’].

Examples

If the input is a list of floats, it will be interpreted as a time-series of spikes to be fed in, one after the other.

neuron.add_spikes([0.0, 1.0, 2.0, 3.0])
# is equivalent to
for i in range(4):
    neuron.add_spike(i, i)

However, 0.0-valued spikes are not added unless exist='overwrite'.

If you would like to send a set of spikes at particular times, you can use a list of tuples:

neuron.add_spikes([
    (1, 1.0),
    (3, 3.0),
])

Note

The times and values will be cast to SNN.default_dtype.

superneuromat.Neuron#

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