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Adaptive Fuzzy Population Coding Method for Spiking Neural Networks

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Abstract

Spiking neural networks (SNNs) process information with temporal coding schemes to transmit feature values into spiking time series. Population Coding (PC) is the most popular scheme, but it fixes static coding parameters and cannot realize the intelligent transformation of information. This paper proposes a new Adaptive Fuzzy Population Coding (AFPC) method, where parameters are optimized synchronously with SNN weights by the learning algorithm to realize coding adaptation. Because the membership function defined in the fuzzy theory is a generalization of the indicator function for classical sets, the receptive field functions used in AFPC are extended to intermediate membership functions instead of a single Gaussian type as in PC. The optimized receptive field functions in AFPC can be really involved in coding, so that the distribution information of training data represented in the spiking time series is more abundant. Numerical experimental results obtained on four benchmark datasets show that AFPC-SNN performs well in terms of classification performance, required number of epochs, generalization and stability. AFPC method can be theoretically and practically applicable to any SNN with SpikeProp algorithm.

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Acknowledgements

This work was supported by the National Key R&D Program of China under Grant 2018AAA0100300, the Fundamental Research Funds for the Central Universities under Grant DUT22YG236, and the National Natural Science Foundation of China under Grant 11201051, 62172073, 62076182.

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Authors and Affiliations

  1. School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China

    Fang Liu, Lu Zhang, Jie Yang & Wei Wu

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  1. Fang Liu
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  2. Lu Zhang
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Correspondence to Jie Yang.

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Liu, F., Zhang, L., Yang, J. et al. Adaptive Fuzzy Population Coding Method for Spiking Neural Networks. Int. J. Fuzzy Syst. 25, 670–683 (2023). https://doi.org/10.1007/s40815-022-01395-9

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  • DOI: https://doi.org/10.1007/s40815-022-01395-9

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