Bio-plausible Unsupervised Delay Learning for Extracting Temporal Features in Spiking Neural Networks

TL;DR

This paper introduces a biologically plausible unsupervised learning rule for adjusting synaptic delays in spiking neural networks, enabling the extraction of temporal features and learning spatio-temporal patterns effectively.

Contribution

It proposes a novel delay learning rule for spiking neural networks, supported by mathematical proofs and experimental validation on temporal pattern recognition tasks.

Findings

The learning rule enables neurons to learn repeating spatio-temporal patterns.

Experimental results show improved temporal feature extraction in SNNs.

The approach aligns with biological plausibility and enhances unsupervised learning capabilities.

Abstract

The plasticity of the conduction delay between neurons plays a fundamental role in learning. However, the exact underlying mechanisms in the brain for this modulation is still an open problem. Understanding the precise adjustment of synaptic delays could help us in developing effective brain-inspired computational models in providing aligned insights with the experimental evidence. In this paper, we propose an unsupervised biologically plausible learning rule for adjusting the synaptic delays in spiking neural networks. Then, we provided some mathematical proofs to show that our learning rule gives a neuron the ability to learn repeating spatio-temporal patterns. Furthermore, the experimental results of applying an STDP-based spiking neural network equipped with our proposed delay learning rule on Random Dot Kinematogram indicate the efficacy of the proposed delay learning rule in extracting temporal features.