BEHAVIORAL FUNCTIONS IMPLEMENTATION ON SPIKING NEURAL NETWORKS

IF 1.9 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Intelligenza Artificiale Pub Date : 2021-06-02 DOI:10.15622/IA.2021.3.4
A. Korsakov, A. Bakhshiev, L. Astapova, L. Stankevich
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引用次数: 1

Abstract

The question of behavioral functions modeling of animals (in particular, the modeling and implementation of the conditioned reflex) is considered. The analysis of the current state of neural networks with the possibility of structural reconfiguration is carried out. The modeling is carried out by means of neural networks, which are built on the basis of a compartmental spiking model of a neuron with the possibility of structural adaptation to the input pulse pattern. The compartmental spike model of a neuron is able to change its structure (the size of the cell body, the number and length of dendrites, the number of synapses) depending on the incoming pulse pattern at its inputs. A brief description of the compartmental spiking model of a neuron is given, and its main features are noted in terms of the possibility of its structural reconfiguration. The method of structural adaptation of the compartmental spiking model of the neuron to the input pulse pattern is described. To study the work of the proposed model of a neuron in a network, the choice of a conditioned reflex as a special case of the formation of associative connections is justified as an example. The structural scheme and algorithm of formation of a conditioned reflex with both positive and negative reinforcement are described. The article presents a step-by-step description of experiments on the associative connection’s formation in general and conditioned reflex (both with positive and negative reinforcement), in particular. The conclusion is made about the prospects of using spiking compartmental models of neurons to improve the efficiency of the implementation of behavioral functions in neuromorphic control systems. Further promising directions for the development of neuromorphic systems based on spiking compartmental models of the neuron are considered.
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脉冲神经网络上行为函数的实现
研究了动物行为功能建模的问题(特别是条件反射的建模和实现)。对具有结构重构可能性的神经网络进行了状态分析。建模是通过神经网络进行的,该神经网络是建立在神经元的室状尖峰模型的基础上的,具有对输入脉冲模式进行结构适应的可能性。神经元的隔室尖峰模型能够根据输入的脉冲模式改变其结构(细胞体的大小、树突的数量和长度、突触的数量)。简要描述了神经元的区室尖峰模型,并就其结构重构的可能性指出了其主要特征。描述了神经元隔室尖峰模型对输入脉冲模式的结构自适应方法。为了研究所提出的网络神经元模型的工作,选择条件反射作为联想连接形成的特殊情况作为一个例子是合理的。描述了正强化和负强化条件反射形成的结构方案和算法。这篇文章提出了一个循序渐进的实验描述的联想连接的形成一般和条件反射(包括正强化和负强化),特别是。最后,展望了利用神经元脉冲区室模型提高神经形态控制系统中行为功能实现效率的前景。展望了基于神经元的脉冲室模型的神经形态系统的进一步发展方向。
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来源期刊
Intelligenza Artificiale
Intelligenza Artificiale COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
3.50
自引率
6.70%
发文量
13
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