Modeling Associative Plasticity between Synapses to Enhance Learning of Spiking Neural Networks

• URL: http://arxiv.org/abs/2207.11670v1
• Date: Sun, 24 Jul 2022 06:12:23 GMT
• Title: Modeling Associative Plasticity between Synapses to Enhance Learning of Spiking Neural Networks
• Authors: Haibo Shen, Juyu Xiao, Yihao Luo, Xiang Cao, Liangqi Zhang, Tianjiang Wang
• Abstract summary: Spiking Neural Networks (SNNs) are the third generation of artificial neural networks that enable energy-efficient implementation on neuromorphic hardware. We propose a robust and effective learning mechanism by modeling the associative plasticity between synapses. Our approaches achieve superior performance on static and state-of-the-art neuromorphic datasets.
• Score: 4.736525128377909
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking Neural Networks (SNNs) are the third generation of artificial neural networks that enable energy-efficient implementation on neuromorphic hardware. However, the discrete transmission of spikes brings significant challenges to the robust and high-performance learning mechanism. Most existing works focus solely on learning between neurons but ignore the influence between synapses, resulting in a loss of robustness and accuracy. To address this problem, we propose a robust and effective learning mechanism by modeling the associative plasticity between synapses (APBS) observed from the physiological phenomenon of associative long-term potentiation (ALTP). With the proposed APBS method, synapses of the same neuron interact through a shared factor when concurrently stimulated by other neurons. In addition, we propose a spatiotemporal cropping and flipping (STCF) method to improve the generalization ability of our network. Extensive experiments demonstrate that our approaches achieve superior performance on static CIFAR-10 datasets and state-of-the-art performance on neuromorphic MNIST-DVS, CIFAR10-DVS datasets by a lightweight convolution network. To our best knowledge, this is the first time to explore a learning method between synapses and an extended approach for neuromorphic data.

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