A Dendritic Neuron Model with Adaptive Synapses Trained by Differential Evolution Algorithm
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- Zhe Wang
- Faculty of Engineering, University of Toyama, Toyama-Shi 930-8555, Japan
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- Shangce Gao
- Faculty of Engineering, University of Toyama, Toyama-Shi 930-8555, Japan
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- Jiaxin Wang
- Faculty of Engineering, University of Toyama, Toyama-Shi 930-8555, Japan
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- Haichuan Yang
- Faculty of Engineering, University of Toyama, Toyama-Shi 930-8555, Japan
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- Yuki Todo
- School of Electrical and Computer Engineering, Kanazawa University, Kanazawa-Shi 920-1192, Japan
説明
<jats:p>A dendritic neuron model with adaptive synapses (DMASs) based on differential evolution (DE) algorithm training is proposed. According to the signal transmission order, a DNM can be divided into four parts: the synaptic layer, dendritic layer, membrane layer, and somatic cell layer. It can be converted to a logic circuit that is easily implemented on hardware by removing useless synapses and dendrites after training. This logic circuit can be designed to solve complex nonlinear problems using only four basic logical devices: comparators, AND (conjunction), OR (disjunction), and NOT (negation). To obtain a faster and better solution, we adopt the most popular DE for DMAS training. We have chosen five classification datasets from the UCI Machine Learning Repository for an experiment. We analyze and discuss the experimental results in terms of the correct rate, convergence rate, ROC curve, and the cross-validation and then compare the results with a dendritic neuron model trained by the backpropagation algorithm (BP-DNM) and a neural network trained by the backpropagation algorithm (BPNN). The analysis results show that the DE-DMAS shows better performance in all aspects.</jats:p>
収録刊行物
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- Computational Intelligence and Neuroscience
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Computational Intelligence and Neuroscience 2020 1-19, 2020-01-17
Hindawi Limited