Efficient Reservoir Encoding Method for Near-Sensor Classification with Rate-Coding Based Spiking Convolutional Neural Networks

  • Xu Yang
  • Shuangming Yu
  • Liyuan LiuEmail author
  • Jian Liu
  • Nanjian WuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)


This paper proposes a general and efficient reservoir encoding method to encode information captured by spike-based and analog-based sensors into spike trains, which helps to realize near-sensor classification with rate-coding based spiking neural networks in real applications. The concept of reservoir is proposed to realize long-term residual information storage while encoding. This method has two configurable parameters, integration time and threshold, and they are determined optimal based on our analysis about encoding requirements. Trough different setting we proposed, reservoir encoding method can be configured as compression mode to compress sparse spike trains obtained from spike-based sensors, or conversion mode to convert pixel values captured by analog-based sensor into spike trains respectively. Verified on MNIST and SVHN dataset, the mapping relationship of information before and after encoding are linear, and the experimental results prove that rate-coding based spiking neural networks with our reservoir encoding method can realize high-accuracy and low-latency classification in two modes.


Rate coding Reservoir encoding Near-sensor classification Spiking neural networks 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Superlattices and MicrostructuresInstitute of Semiconductors, Chinese Academy of SciencesBeijingPeople’s Republic of China
  2. 2.Center of Materials Science and Optoelectronics EngineeringUniversity of Chinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesBeijingPeople’s Republic of China

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