Multi-Activation Dendritic Neural Network (MA-DNN) Working Example of Dendritic-Based Artificial Neural Network

IF 1.2 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Konstantin Tomov, Galina Momcheva
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引用次数: 0

Abstract

Abstract Throughout the years neural networks have been based on the perceptron model of the artificial neuron. Attempts to stray from it are few to none. The perceptron simply works and that has discouraged research around other neuron models. New discoveries highlight the importance of dendrites in the neuron, but the perceptron model does not include them. This brings us to the goal of the paper which is to present and test different models of artificial neurons that utilize dendrites to create an artificial neuron that better represents the biological neuron. The authors propose two models. One is made with the purpose of testing the idea of the dendritic neuron. The distinguishing feature of the second model is that it implements activation functions after its dendrites. Results from the second model suggest that it performs as well as or even better than the perceptron model.
多激活树突神经网络(MA-DNN)基于树突的人工神经网络工作实例
多年来,神经网络一直是基于人工神经元的感知器模型。试图偏离它的人很少,甚至没有。感知器只是简单地工作,这阻碍了对其他神经元模型的研究。新的发现强调了神经元中树突的重要性,但感知器模型并没有包括它们。这将我们带到了本文的目标,即展示和测试不同的人工神经元模型,这些模型利用树突来创建一个更好地代表生物神经元的人工神经元。作者提出了两个模型。一个是为了测试树突神经元的概念。第二种模型的显著特点是在树突之后实现激活函数。第二个模型的结果表明,它的性能与感知器模型一样好,甚至更好。
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来源期刊
Cybernetics and Information Technologies
Cybernetics and Information Technologies COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.20
自引率
25.00%
发文量
35
审稿时长
12 weeks
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