Neurons and Layers

Neurons in spikingnet are grouped in layers. Here we document the basic layer interface as well as the types of layers implemented in spikingnet.

The layer interface

All layers in spikingnet share the same underlying interface. When a layer is added to a SpikingNet object through its add_layer method, it expects the layer to conform to the following interface:

  • Layers should have a __call__ method implemented returning one output.

  • Layers should have reset and update methods implemented. The reset method is called to return the network and the layer to its initial state. update methods are triggered during learning.

Neuron models

class spikelearn.neurons.LIFLayer(N, tau, v0=1, refr=True)

Implements a leaky integrate and fire

Implements a layer of LIF neurons. Its interface conforms to that of a Layer object.

Parameters:

  • N – Neurons in the layer

  • tau – decay time, in timestep units

  • v0 – threshold value (optional, default 1)

  • refr – boolean, neuron has 1 timestep refractory period

property N

Neurons in the layer

__call__(*x)

Advances the neuron a single timestep

Parameters:

x – a tuple of independent inputs

Returns:

An array of spikes, 1 if a neuron spikes 0 otherwise

property out

Output spikes

reset()

Resets the neuron internal state

property s

Output spikes

step(*x)

Advances the neuron a single timestep

Parameters:

x – a tuple of independent inputs

Returns:

An array of spikes, 1 if a neuron spikes 0 otherwise

property tau

Leakage time in timesteps

property v

Membrane potential

spikelearn.neurons.SpikingLayer

alias of LIFLayer

class spikelearn.neurons.SpikingRecLayer(N, tau, Wrec, v0=1)

Implements spiking neurons with an internal recurrent interaction.

Implements a layer of leaky integrate and fire (LIF) neurons with an additional static, recurrent interaction within the layer. This layer can be used to implement cross-inhibition between neurons within the layer. Its interface conforms to that of a Layer object.

Parameters:

  • N – number of neurons in the layer

  • tau – decay time, in timestep units

  • Wrec – a 2D array with synaptic weights

  • v0 (optional, default 1) – threshold value

property N

Neurons in the layer

__call__(*x)

Advances the neuron a single timestep

Parameters:

x – a tuple of independent inputs

Returns:

An array of spikes, 1 if a neuron spikes 0 otherwise

property out

Output spikes

reset()

Resets the neuron internal state

property s

Output spikes

step(*x)

Advances the neuron a single timestep

Parameters:

x – a tuple of independent inputs

Returns:

An array of spikes, 1 if a neuron spikes 0 otherwise

property tau

Leakage time in timesteps

property v

Membrane potential