Efficient Event-based Delay Learning in Spiking Neural Networks

• URL: http://arxiv.org/abs/2501.07331v2
• Date: Fri, 31 Jan 2025 16:26:19 GMT
• Title: Efficient Event-based Delay Learning in Spiking Neural Networks
• Authors: Balázs Mészáros, James C. Knight, Thomas Nowotny,
• Abstract summary: Spiking Neural Networks (SNNs) are attracting increased attention as an energy-efficient alternative to traditional Neural Networks. We propose a novel event-based training method for SNNs, grounded in the EventPropProp formalism. We show that our approach uses less than half the memory of the current state-of-the-art delay-learning method and is up to 26x faster.
• Score: 0.1350479308585481
• License:
• Abstract: Spiking Neural Networks (SNNs) are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks. Spiking neurons are stateful and intrinsically recurrent, making them well-suited for spatio-temporal tasks. However, this intrinsic memory is limited by synaptic and membrane time constants. A powerful additional mechanism are delays. In this paper, we propose a novel event-based training method for SNNs with delays, grounded in the EventProp formalism and enabling the calculation of exact gradients with respect to weights and delays. Our method supports multiple spikes per neuron and, to our best knowledge, is the first delay learning algorithm to be applied to recurrent SNNs. We evaluate our method on a simple sequence detection task, and the Yin-Yang, Spiking Heidelberg Digits and Spiking Speech Commands datasets, demonstrating that our algorithm can optimize delays from suboptimal initial conditions and enhance classification accuracy compared to architectures without delays. Finally, we show that our approach uses less than half the memory of the current state-of-the-art delay-learning method and is up to 26x faster.

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