机器人代理工作环境描述中联想记忆形成的神经形态模型

A. Korsakov
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引用次数: 0

摘要

本文提出了一种基于神经形态原理的机器人智能体工作空间描述方法。采用具有结构自适应可能性的神经元分段尖峰模型作为建模的基本元素。指出了神经元模型在结构重构可能性方面的主要特征。作为这种描述的模型的基础,我们选择了一种在生物体中形成条件反射的方案。给出了条件反射形成的神经形态模型的结构方案,以及联想记忆形成模型的一般方案。给出了在这种方案中形成关联链接的算法的一步一步的描述。提出了当几个对象中出现竞争性特征时,抑制连接形成的原理。本文介绍了一个模型实例的计算机模拟结果。总结了所选神经元模型的适用性,以及将神经元组织成网络来解决机器人代理工作空间描述问题的方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neuromorphic model of associative memory in the formation of the working environment description of a robotic agent
The article considers an approach to solving the problem of describing the workspace of a robotic agent based on the neuromorphic principle. A segmental spike model of a neuron with the possibility of structural adaptation was used as a basic element in the modeling. The main features of the neuron model used in terms of the possibilities of its structural reconfiguration are indicated. As the basis of the model for such a description, a scheme for the formation of a conditioned reflex in living organisms is chosen. The structural scheme of the neuromorphic model of conditioned reflex formation is given, as well as the general scheme of the model of associative memory formation. A step-by-step description of the algorithm for forming associative links in such a scheme is given. The principle of the formation of inhibitory connections when competing features appear in several objects is presented. The article presents the results of computer modeling on a model example. The conclusion is made about the applicability of the chosen neuron model and the scheme of organizing neurons into a network to solve the problem of describing the workspace of a robotic agent.
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