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Exploring tradeoffs in spiking neural networks

Bacho, Florian, Chu, Dominique (2023) Exploring tradeoffs in spiking neural networks. Neural Computation, 35 (10). pp. 1627-1656. ISSN 0899-7667. (doi:10.1162/neco_a_01609) (KAR id:101545)


Spiking Neural Networks (SNNs) have emerged as a promising alternative to traditional Deep Neural Networks for low-power computing. However, the effectiveness of SNNs is not solely determined by their performance but also by their energy consumption, prediction speed, and robustness to noise. The recent method Fast & Deep, along with others, achieves fast and energy-efficient computation by constraining neurons to fire

at most once. Known as Time-To-First-Spike (TTFS), this constraint however restricts the capabilities of SNNs in many aspects. In this work, we explore the relationships between performance, energy consumption, speed and stability when using this constraint. More precisely, we highlight the existence of tradeoffs where performance and robustness are gained at the cost of sparsity and prediction latency. To improve these tradeoffs, we propose a relaxed version of Fast & Deep that allows for multiple spikes per neuron. Our experiments show that relaxing the spike constraint provides higher

performance while also benefiting from faster convergence, similar sparsity, comparable prediction latency, and better robustness to noise compared to TTFS SNNs. By highlighting the limitations of TTFS and demonstrating the advantages of unconstrained SNNs we provide valuable insight for the development of effective learning strategies for neuromorphic computing.

Item Type: Article
DOI/Identification number: 10.1162/neco_a_01609
Uncontrolled keywords: Spiking Neural Networks; Tradeoffs; Time-To-First-Spike; Error Backpropagation
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: University of Kent (
Depositing User: Florian Bacho
Date Deposited: 05 Jun 2023 12:52 UTC
Last Modified: 18 Oct 2023 10:14 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Bacho, Florian.

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Chu, Dominique.

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