Training Probabilistic Spiking Neural Networks with First-to-spike Decoding

TL;DR

This paper introduces a novel training method for two-layer Spiking Neural Networks that enables early classification decisions based on first spike detection, improving efficiency while maintaining accuracy.

Contribution

It proposes a new training approach for first-to-spike decoding in SNNs, contrasting with traditional offline spike count methods.

Findings

Optimal parameter settings for GLM neurons identified.

First-to-spike decoding achieves competitive accuracy.

Trade-offs between accuracy and complexity analyzed.

Abstract

Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes. In this paper, the problem of training a two-layer SNN is studied for the purpose of classification, under a Generalized Linear Model (GLM) probabilistic neural model that was previously considered within the computational neuroscience literature. Conventional classification rules for SNNs operate offline based on the number of output spikes at each output neuron. In contrast, a novel training method is proposed here for a first-to-spike decoding rule, whereby the SNN can perform an early classification decision once spike firing is detected at an output neuron. Numerical results bring insights into the optimal parameter selection for the GLM neuron and on the accuracy-complexity trade-off performance of conventional and first-to-spike decoding.