Toward Efficient Processing and Learning with Spikes : New Approaches for Multispike Learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

12 Scopus Citations
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  • Qiang Yu
  • Shenglan Li
  • Huajin Tang
  • Longbiao Wang
  • Jianwu Dang


Original languageEnglish
Pages (from-to)1364-1376
Journal / PublicationIEEE Transactions on Cybernetics
Issue number3
Online published29 Apr 2020
Publication statusPublished - Mar 2022


Spikes are the currency in central nervous systems for information transmission and processing. They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the field of neuromorphic computing. However, efficient processing and learning of discrete spikes still remain a challenging problem. In this article, we make our contributions toward this direction. A simplified spiking neuron model is first introduced with the effects of both synaptic input and firing output on the membrane potential being modeled with an impulse function. An event-driven scheme is then presented to further improve the processing efficiency. Based on the neuron model, we propose two new multispike learning rules which demonstrate better performance over other baselines on various tasks, including association, classification, and feature detection. In addition to efficiency, our learning rules demonstrate high robustness against the strong noise of different types. They can also be generalized to different spike coding schemes for the classification task, and notably, the single neuron is capable of solving multicategory classifications with our learning rules. In the feature detection task, we re-examine the ability of unsupervised spike-timing-dependent plasticity with its limitations being presented, and find a new phenomenon of losing selectivity. In contrast, our proposed learning rules can reliably solve the task over a wide range of conditions without specific constraints being applied. Moreover, our rules cannot only detect features but also discriminate them. The improved performance of our methods would contribute to neuromorphic computing as a preferable choice.

Research Area(s)

  • Feature extraction, multispike learning, neuromorphic computing, robust recognition, spike-timing-dependent plasticity (STDP), spiking neural networks (SNNs)

Citation Format(s)

Toward Efficient Processing and Learning with Spikes: New Approaches for Multispike Learning. / Yu, Qiang; Li, Shenglan; Tang, Huajin et al.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 3, 03.2022, p. 1364-1376.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review