SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training

Spiking Neural Networks (SNNs) are a pathway that could potentially empower low-power event-driven neuromorphic hardware due to their spatio-temporal information processing capability and high biological plausibility. Although SNNs are currently more efficient than artificial neural networks (ANNs),...

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Autores principales:	Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Tao Yang, Li Jiang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	spiking neural network gradient descent backpropagation neuromorphic computing spike-time-dependent plasticity deep learning efficient training Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/f6a5d3a0ea4e4e48a939a0bc137d6667
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spelling	oai:doaj.org-article:f6a5d3a0ea4e4e48a939a0bc137d66672021-11-05T16:11:44ZSSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training1662-453X10.3389/fnins.2021.756876https://doaj.org/article/f6a5d3a0ea4e4e48a939a0bc137d66672021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.756876/fullhttps://doaj.org/toc/1662-453XSpiking Neural Networks (SNNs) are a pathway that could potentially empower low-power event-driven neuromorphic hardware due to their spatio-temporal information processing capability and high biological plausibility. Although SNNs are currently more efficient than artificial neural networks (ANNs), they are not as accurate as ANNs. Error backpropagation is the most common method for directly training neural networks, promoting the prosperity of ANNs in various deep learning fields. However, since the signals transmitted in the SNN are non-differentiable discrete binary spike events, the activation function in the form of spikes presents difficulties for the gradient-based optimization algorithms to be directly applied in SNNs, leading to a performance gap (i.e., accuracy and latency) between SNNs and ANNs. This paper introduces a new learning algorithm, called SSTDP, which bridges the gap between backpropagation (BP)-based learning and spike-time-dependent plasticity (STDP)-based learning to train SNNs efficiently. The scheme incorporates the global optimization process from BP and the efficient weight update derived from STDP. It not only avoids the non-differentiable derivation in the BP process but also utilizes the local feature extraction property of STDP. Consequently, our method can lower the possibility of vanishing spikes in BP training and reduce the number of time steps to reduce network latency. In SSTDP, we employ temporal-based coding and use Integrate-and-Fire (IF) neuron as the neuron model to provide considerable computational benefits. Our experiments show the effectiveness of the proposed SSTDP learning algorithm on the SNN by achieving the best classification accuracy 99.3% on the Caltech 101 dataset, 98.1% on the MNIST dataset, and 91.3% on the CIFAR-10 dataset compared to other SNNs trained with other learning methods. It also surpasses the best inference accuracy of the directly trained SNN with 25~32× less inference latency. Moreover, we analyze event-based computations to demonstrate the efficacy of the SNN for inference operation in the spiking domain, and SSTDP methods can achieve 1.3~37.7× fewer addition operations per inference. The code is available at: https://github.com/MXHX7199/SNN-SSTDP.Fangxin LiuFangxin LiuWenbo ZhaoWenbo ZhaoYongbiao ChenZongwu WangTao YangLi JiangLi JiangLi JiangFrontiers Media S.A.articlespiking neural networkgradient descent backpropagationneuromorphic computingspike-time-dependent plasticitydeep learningefficient trainingNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	spiking neural network gradient descent backpropagation neuromorphic computing spike-time-dependent plasticity deep learning efficient training Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	spiking neural network gradient descent backpropagation neuromorphic computing spike-time-dependent plasticity deep learning efficient training Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Fangxin Liu Fangxin Liu Wenbo Zhao Wenbo Zhao Yongbiao Chen Zongwu Wang Tao Yang Li Jiang Li Jiang Li Jiang SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training
description	Spiking Neural Networks (SNNs) are a pathway that could potentially empower low-power event-driven neuromorphic hardware due to their spatio-temporal information processing capability and high biological plausibility. Although SNNs are currently more efficient than artificial neural networks (ANNs), they are not as accurate as ANNs. Error backpropagation is the most common method for directly training neural networks, promoting the prosperity of ANNs in various deep learning fields. However, since the signals transmitted in the SNN are non-differentiable discrete binary spike events, the activation function in the form of spikes presents difficulties for the gradient-based optimization algorithms to be directly applied in SNNs, leading to a performance gap (i.e., accuracy and latency) between SNNs and ANNs. This paper introduces a new learning algorithm, called SSTDP, which bridges the gap between backpropagation (BP)-based learning and spike-time-dependent plasticity (STDP)-based learning to train SNNs efficiently. The scheme incorporates the global optimization process from BP and the efficient weight update derived from STDP. It not only avoids the non-differentiable derivation in the BP process but also utilizes the local feature extraction property of STDP. Consequently, our method can lower the possibility of vanishing spikes in BP training and reduce the number of time steps to reduce network latency. In SSTDP, we employ temporal-based coding and use Integrate-and-Fire (IF) neuron as the neuron model to provide considerable computational benefits. Our experiments show the effectiveness of the proposed SSTDP learning algorithm on the SNN by achieving the best classification accuracy 99.3% on the Caltech 101 dataset, 98.1% on the MNIST dataset, and 91.3% on the CIFAR-10 dataset compared to other SNNs trained with other learning methods. It also surpasses the best inference accuracy of the directly trained SNN with 25~32× less inference latency. Moreover, we analyze event-based computations to demonstrate the efficacy of the SNN for inference operation in the spiking domain, and SSTDP methods can achieve 1.3~37.7× fewer addition operations per inference. The code is available at: https://github.com/MXHX7199/SNN-SSTDP.
format	article
author	Fangxin Liu Fangxin Liu Wenbo Zhao Wenbo Zhao Yongbiao Chen Zongwu Wang Tao Yang Li Jiang Li Jiang Li Jiang
author_facet	Fangxin Liu Fangxin Liu Wenbo Zhao Wenbo Zhao Yongbiao Chen Zongwu Wang Tao Yang Li Jiang Li Jiang Li Jiang
author_sort	Fangxin Liu
title	SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training
title_short	SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training
title_full	SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training
title_fullStr	SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training
title_full_unstemmed	SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training
title_sort	sstdp: supervised spike timing dependent plasticity for efficient spiking neural network training
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/f6a5d3a0ea4e4e48a939a0bc137d6667
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SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training

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