SPA: Stochastic Probability Adjustment for System Balance of Unsupervised SNNs

• URL: http://arxiv.org/abs/2010.09690v2
• Date: Thu, 6 May 2021 07:53:42 GMT
• Title: SPA: Stochastic Probability Adjustment for System Balance of Unsupervised SNNs
• Authors: Xingyu Yang, Mingyuan Meng, Shanlin Xiao, and Zhiyi Yu
• Abstract summary: Spiking neural networks (SNNs) receive widespread attention because of their low-power hardware characteristic and brain-like signal response mechanism. We build an information theory-inspired system called Probability Adjustment (SPA) to reduce this gap. The improvements in classification accuracy have reached 1.99% and 6.29% on the MNIST and EMNIST datasets respectively.
• Score: 2.729898906885749
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Spiking neural networks (SNNs) receive widespread attention because of their low-power hardware characteristic and brain-like signal response mechanism, but currently, the performance of SNNs is still behind Artificial Neural Networks (ANNs). We build an information theory-inspired system called Stochastic Probability Adjustment (SPA) system to reduce this gap. The SPA maps the synapses and neurons of SNNs into a probability space where a neuron and all connected pre-synapses are represented by a cluster. The movement of synaptic transmitter between different clusters is modeled as a Brownian-like stochastic process in which the transmitter distribution is adaptive at different firing phases. We experimented with a wide range of existing unsupervised SNN architectures and achieved consistent performance improvements. The improvements in classification accuracy have reached 1.99% and 6.29% on the MNIST and EMNIST datasets respectively.

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