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An Evolutionary Neuron Model with Dendritic Computation for Classification and Prediction

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Intelligent Computing Theories and Application (ICIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12836))

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Abstract

Advances in the understanding of dendrites promote the development of dendritic computation. For decades, the researchers are committed to proposing an appropriate neural model, which may feedback the research on neurons. This paper aims to employ an effective metaheuristic optimization algorithm as the learning algorithms to train the dendritic neuron model (DNM). The powerful ability of the backpropagation (BP) algorithm to train artificial neural networks led us to employ it as a learning algorithm for a conventional DNM, but this also inevitably causes the DNM to suffer from the drawbacks of the algorithm. Therefore, a metaheuristic optimization algorithm, named the firefly algorithm (FA) is adopted to train the DNM (FADNM). Experiments on twelve datasets involving classification and prediction are performed to evaluate the performance. The experimental results and corresponding statistical analysis show that the learning algorithm plays a decisive role in the performance of the DNM. It is worth emphasizing that the FADNM incorporates an invaluable neural pruning scheme to eliminate superfluous synapses and dendrites, simplifying its structure and forming a unique morphology. This simplified morphology can be implemented in hardware through logic circuits, which approximately has no effect on the accuracy of the original model. The hardwareization enables the FADNM to efficiently process high-speed data streams for large-scale data, which leads us to believe that it might be a promising technology to deal with big data.

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Acknowledgment

This work was supported by a Project of the Guangdong Basic and Applied Basic Research Fund (No. 2019A1515111139), the Nature Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 19KJB520015), the Talent Development Project of Taizhou University (No. TZXY2018QDJJ006), and the general item of Hunan philosophy and Social Science Foundation (20YBA260), namely Research on Financial Risk Management of Supply Chain in Hunan Free Trade Zone with Artificial Intelligence. The authors would like to thank the Otsuka Toshimi Scholarship Foundation for its support.

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Authors and Affiliations

  1. Faculty of Engineering, University of Toyama, Toyama, 930-8555, Japan

    Cheng Tang

  2. College of Computer Science and Technology, Taizhou University, Taizhou, 225300, China

    Zhenyu Song

  3. College of Economics, Central South University of Forestry and Technology, Changsha, 410004, China

    Yajiao Tang

  4. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Huimei Tang & Junkai Ji

  5. Taizhou People’s Hospital, Taizhou, 225300, China

    Yuxi Wang

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  1. Cheng Tang
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  3. Yajiao Tang
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  4. Huimei Tang
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  6. Junkai Ji
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Corresponding author

Correspondence to Junkai Ji .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Shenzhen University, Shenzhen, China

    Jianqiang Li

  4. Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Valeriya Gribova

  5. Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

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Tang, C., Song, Z., Tang, Y., Tang, H., Wang, Y., Ji, J. (2021). An Evolutionary Neuron Model with Dendritic Computation for Classification and Prediction. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-84522-3_2

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  • Print ISBN: 978-3-030-84521-6

  • Online ISBN: 978-3-030-84522-3

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