Gradient Scaling on Deep Spiking Neural Networks with Spike-Dependent Local Information

• URL: http://arxiv.org/abs/2308.00558v1
• Date: Tue, 1 Aug 2023 13:58:21 GMT
• Title: Gradient Scaling on Deep Spiking Neural Networks with Spike-Dependent Local Information
• Authors: Seongsik Park, Jeonghee Jo, Jongkil Park, Yeonjoo Jeong, Jaewook Kim, Suyoun Lee, Joon Young Kwak, Inho Kim, Jong-Keuk Park, Kyeong Seok Lee, Gye Weon Hwang, Hyun Jae Jang
• Abstract summary: We train deep neural networks (SNNs) with spiking backpropagation (STBP) with surrogate gradient. In this work, we proposed gradient with scaling local spike information, which is the relation between pre- and post-temporal spikes. Considering the causality between spikes, we could enhance the training of deep SNNs.
• Score: 2.111711135667053
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep spiking neural networks (SNNs) are promising neural networks for their model capacity from deep neural network architecture and energy efficiency from SNNs' operations. To train deep SNNs, recently, spatio-temporal backpropagation (STBP) with surrogate gradient was proposed. Although deep SNNs have been successfully trained with STBP, they cannot fully utilize spike information. In this work, we proposed gradient scaling with local spike information, which is the relation between pre- and post-synaptic spikes. Considering the causality between spikes, we could enhance the training performance of deep SNNs. According to our experiments, we could achieve higher accuracy with lower spikes by adopting the gradient scaling on image classification tasks, such as CIFAR10 and CIFAR100.

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