Supervised Learning with First-to-Spike Decoding in Multilayer Spiking Neural Networks

TL;DR

This paper introduces a supervised learning method for multilayer spiking neural networks that uses a first-to-spike decoding strategy, enabling efficient classification with compact, spike-based input representations suitable for neuromorphic systems.

Contribution

It proposes a novel stable learning rule supporting multiple spikes and introduces a new encoding strategy called scanline encoding for efficient data representation.

Findings

Achieves competitive classification performance on benchmark datasets like MNIST.

Supports networks with few neurons, demonstrating efficiency and generalization.

Introduces scanline encoding for compact spatiotemporal input representation.

Abstract

Experimental studies support the notion of spike-based neuronal information processing in the brain, with neural circuits exhibiting a wide range of temporally-based coding strategies to rapidly and efficiently represent sensory stimuli. Accordingly, it would be desirable to apply spike-based computation to tackling real-world challenges, and in particular transferring such theory to neuromorphic systems for low-power embedded applications. Motivated by this, we propose a new supervised learning method that can train multilayer spiking neural networks to solve classification problems based on a rapid, first-to-spike decoding strategy. The proposed learning rule supports multiple spikes fired by stochastic hidden neurons, and yet is stable by relying on first-spike responses generated by a deterministic output layer. In addition to this, we also explore several distinct, spike-based encoding strategies in order to form compact representations of presented input data. We demonstrate the classification performance of the learning rule as applied to several benchmark datasets, including MNIST. The learning rule is capable of generalising from the data, and is successful even when used with constrained network architectures containing few input and hidden layer neurons. Furthermore, we highlight a novel encoding strategy, termed `scanline encoding', that can transform image data into compact spatiotemporal patterns for subsequent network processing. Designing constrained, but optimised, network structures and performing input dimensionality reduction has strong implications for neuromorphic applications.