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Supervised Learning Algorithm Based on Spike Train Inner Product for Deep Spiking Neural Networks

Brain sciences, 2023-01, Vol.13 (2), p.168 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2023 by the authors. 2023 ;ISSN: 2076-3425 ;EISSN: 2076-3425 ;DOI: 10.3390/brainsci13020168 ;PMID: 36831711

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  • Title:
    Supervised Learning Algorithm Based on Spike Train Inner Product for Deep Spiking Neural Networks
  • Author: Lin, Xianghong ; Zhang, Zhen ; Zheng, Donghao
  • Subjects: Algorithms ; Approximation ; Back propagation ; Brain research ; Computational linguistics ; Data mining ; Deep learning ; Feedback ; feedback alignment mechanism ; Firing pattern ; Information processing ; Information storage ; Language processing ; Learning ; Machine learning ; Natural language interfaces ; Neural networks ; Neurons ; Neurophysiology ; Pattern recognition ; Signal processing ; spike train inner product ; spiking neural networks
  • Is Part Of: Brain sciences, 2023-01, Vol.13 (2), p.168
  • Description: By mimicking the hierarchical structure of human brain, deep spiking neural networks (DSNNs) can extract features from a lower level to a higher level gradually, and improve the performance for the processing of spatio-temporal information. Due to the complex hierarchical structure and implicit nonlinear mechanism, the formulation of spike train level supervised learning methods for DSNNs remains an important problem in this research area. Based on the definition of kernel function and spike trains inner product (STIP) as well as the idea of error backpropagation (BP), this paper firstly proposes a deep supervised learning algorithm for DSNNs named BP-STIP. Furthermore, in order to alleviate the intrinsic weight transport problem of the BP mechanism, feedback alignment (FA) and broadcast alignment (BA) mechanisms are utilized to optimize the error feedback mode of BP-STIP, and two deep supervised learning algorithms named FA-STIP and BA-STIP are also proposed. In the experiments, the effectiveness of the proposed three DSNN algorithms is verified on the MNIST digital image benchmark dataset, and the influence of different kernel functions on the learning performance of DSNNs with different network scales is analyzed. Experimental results show that the FA-STIP and BP-STIP algorithms can achieve 94.73% and 95.65% classification accuracy, which apparently possess better learning performance and stability compared with the benchmark algorithm BP-STIP.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 2076-3425
    EISSN: 2076-3425
    DOI: 10.3390/brainsci13020168
    PMID: 36831711
  • Source: PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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