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ConvSNP: a deep learning model embedded with SNP-like neurons

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Abstract

Inspired from spiking mechanisms in spiking neural P (SNP) systems, this paper proposes a new type of neurons, termed as SNP-like neurons. The mathematical model for SNP-like neurons is a generalized linear function. Based on SNP-like neurons, a new class of deep learning models are developed, called ConvSNP models. By referring the structures of the existing convolutional neural networks (CNNs), five ConvSNP models are designed. The five ConvSNP models are evaluated on three benchmark data sets and compared with the corresponding CNNs. The comparison results demonstrate the availability and effectiveness of ConvSNP models for three classical classification tasks.

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (No. 62176216 and No. 62076206), China.

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Authors and Affiliations

  1. School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China

    Shuwei Zhao, Li Zhang, Zhicai Liu & Hong Peng

  2. School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, 610039, China

    Jun Wang

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  1. Shuwei Zhao
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  2. Li Zhang
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  3. Zhicai Liu
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  4. Hong Peng
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  5. Jun Wang
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Correspondence to Hong Peng.

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Zhao, S., Zhang, L., Liu, Z. et al. ConvSNP: a deep learning model embedded with SNP-like neurons. J Membr Comput 4, 87–95 (2022). https://doi.org/10.1007/s41965-022-00094-6

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  • DOI: https://doi.org/10.1007/s41965-022-00094-6

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