Improving Surrogate Gradient Learning in Spiking Neural Networks via Regularization and Normalization

• URL: http://arxiv.org/abs/2201.02538v1
• Date: Mon, 13 Dec 2021 15:24:33 GMT
• Title: Improving Surrogate Gradient Learning in Spiking Neural Networks via Regularization and Normalization
• Authors: Nandan Meda
• Abstract summary: Spiking neural networks (SNNs) are different from the classical networks used in deep learning. SNNs are appealing for AI technology, because they could be implemented on low power neuromorphic chips.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking neural networks (SNNs) are different from the classical networks used in deep learning: the neurons communicate using electrical impulses called spikes, just like biological neurons. SNNs are appealing for AI technology, because they could be implemented on low power neuromorphic chips. However, SNNs generally remain less accurate than their analog counterparts. In this report, we examine various regularization and normalization techniques with the goal of improving surrogate gradient learning in SNNs.

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