Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN

TL;DR

This paper introduces a novel Lyapunov Noise Pruning method for designing sparse, heterogeneous recurrent spiking neural networks from random initialization, improving efficiency and performance without task-specific training.

Contribution

The paper presents a task-agnostic pruning approach using Lyapunov exponents and graph sparsification to create efficient sparse RSNNs with diverse timescales.

Findings

LNP outperforms activity-based pruning in efficiency and accuracy.

Sparse HRSNNs generalize across different tasks.

Method reduces neurons and synapses while maintaining performance.

Abstract

Recurrent Spiking Neural Networks (RSNNs) have emerged as a computationally efficient and brain-inspired learning model. The design of sparse RSNNs with fewer neurons and synapses helps reduce the computational complexity of RSNNs. Traditionally, sparse SNNs are obtained by first training a dense and complex SNN for a target task, and, then, pruning neurons with low activity (activity-based pruning) while maintaining task performance. In contrast, this paper presents a task-agnostic methodology for designing sparse RSNNs by pruning a large randomly initialized model. We introduce a novel Lyapunov Noise Pruning (LNP) algorithm that uses graph sparsification methods and utilizes Lyapunov exponents to design a stable sparse RSNN from a randomly initialized RSNN. We show that the LNP can leverage diversity in neuronal timescales to design a sparse Heterogeneous RSNN (HRSNN). Further, we show that the same sparse HRSNN model can be trained for different tasks, such as image classification and temporal prediction. We experimentally show that, in spite of being task-agnostic, LNP increases computational efficiency (fewer neurons and synapses) and prediction performance of RSNNs compared to traditional activity-based pruning of trained dense models.