Yet Another Effective Dendritic Neuron Model Based on the Activity of Excitation and Inhibition

Yifei Yang, Xiaosi Li, Haotian Li, Chaofeng Zhang, Yuki Todo () and Haichuan Yang ()
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Yifei Yang: Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
Xiaosi Li: Department of Engineering, Wesoft Company Ltd., Kawasaki 210-0024, Japan
Haotian Li: Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
Chaofeng Zhang: Advanced Institute of Industrial Technology, Tokyo 140-0011, Japan
Yuki Todo: Faculty of Electrical and Computer Engineering, Kanazawa University, Kanazawa 920-1192, Japan
Haichuan Yang: Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan

Mathematics, 2023, vol. 11, issue 7, 1-23

Abstract: Neuronal models have remained an important area of research in computer science. The dendritic neuron model (DNM) is a novel neuronal model in recent years. Previous studies have focused on training DNM using more appropriate algorithms. This paper proposes an improvement to DNM based on the activity of excitation and proposes three new models. Each of the three improved models are designed to mimic the excitation and inhibition activity of neurons. The improved model proposed in this paper is shown to be effective in the experimental part. All three models and original DNM have their own strengths, so it can be considered that the new model proposed in this paper well enriches the diversity of neuronal models and contributes to future research on networks models.

Keywords: dendritic neuron model; classification problems; evolutionary algorithms; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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