A Plasticity-Centric Approach to Train the Non-Differential Spiking Neural Networks

Authors

  • Tielin Zhang Institute of Automation, Chinese Academy of Sciences
  • Yi Zeng Institute of Automation, Chinese Academy of Sciences
  • Dongcheng Zhao Institute of Automation, Chinese Academy of Sciences
  • Mengting Shi Institute of Automation, Chinese Academy of Sciences

Keywords:

Spiking neural network, Unsupervised learning, Supervised learning

Abstract

Many efforts have been taken to train spiking neural networks (SNNs), but most of them still need improvements due to the discontinuous and non-differential characteristics of SNNs. While the mammalian brains solve these kinds of problems by integrating a series of biological plasticity learning rules. In this paper, we will focus on two biological plausible methodologies and try to solve these catastrophic training problems in SNNs. Firstly, the biological neural network will try to keep a balance between inputs and outputs on both the neuron and the network levels. Secondly, the biological synaptic weights will be passively updated by the changes of the membrane potentials of the neighbour-hood neurons, and the plasticity of synapses will not propagate back to other previous layers. With these biological inspirations, we propose Voltage-driven Plasticity-centric SNN (VPSNN), which includes four steps, namely: feed forward inference, unsupervised equilibrium state learning, supervised last layer learning and passively updating synaptic weights based on spike-timing dependent plasticity (STDP). Finally we get the accuracy of 98.52% on the hand-written digits classification task on MNIST. In addition, with the help of a visualization tool, we try to analyze the black box of SNN and get better understanding of what benefits have been acquired by the proposed method.

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Published

2018-04-25

How to Cite

Zhang, T., Zeng, Y., Zhao, D., & Shi, M. (2018). A Plasticity-Centric Approach to Train the Non-Differential Spiking Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11317

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Section

AAAI Technical Track: Cognitive Modeling