Co-learning synaptic delays, weights and adaptation in spiking neural
  networks
Lucas Deckers Laurens Van Damme Ing Jyh Tsang Werner Van Leekwijck Steven Latré
Spiking neural networks (SNN) distinguish themselves from artificial neural
networks (ANN) because of their inherent temporal processing and spike-based
computations, enabling a power-efficient implementation in neuromorphic
hardware. In this paper, we demonstrate that data processing with spiking
neurons can be enhanced by co-learning the connection weights with two other
biologically inspired neuronal features: 1) a set of parameters describing
neuronal adaptation processes and 2) synaptic propagation delays. The former
allows the spiking neuron to learn how to specifically react to incoming spikes
based on its past. The trained adaptation parameters result in neuronal
heterogeneity, which is found in the brain and also leads to a greater variety
in available spike patterns. The latter enables to learn to explicitly
correlate patterns that are temporally distanced. Synaptic delays reflect the
time an action potential requires to travel from one neuron to another. We show
that each of the co-learned features separately leads to an improvement over
the baseline SNN and that the combination of both leads to state-of-the-art SNN
results on all speech recognition datasets investigated with a simple 2-hidden
layer feed-forward network. Our SNN outperforms the ANN on the neuromorpic
datasets (Spiking Heidelberg Digits and Spiking Speech Commands), even with
fewer trainable parameters. On the 35-class Google Speech Commands dataset, our
SNN also outperforms a GRU of similar size. Our work presents brain-inspired
improvements to SNN that enable them to excel over an equivalent ANN of similar
size on tasks with rich temporal dynamics.