Hopfield网络方法中的神经流

C. Ionescu, E. Panaitescu, Mihai Stoicescu
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引用次数: 1

摘要

在大多数涉及神经网络的应用中,主要问题在于找到一个最优的过程来将真实神经元简化为更简单的模型,这些模型既能表达生物复杂性,又能突出系统的主要特征。我们有效地研究了从复杂的Hodgkin-Huxley型模型到非常方便的Hopfield型二元模型的简单约简过程。这种约简将允许描述一个相当大的网络中的神经元互连,并获得有关其对称性和稳定性的信息。这两种情况下,均匀电压跨膜和不均匀电压沿轴突将处理出来。本文还将介绍一些基于索方程的神经流数值模拟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural Flows in Hopfield Network Approach
Abstract In most of the applications involving neural networks, the main problem consists in finding an optimal procedure to reduce the real neuron to simpler models which still express the biological complexity but allow highlighting the main characteristics of the system. We effectively investigate a simple reduction procedure which leads from complex models of Hodgkin-Huxley type to very convenient binary models of Hopfield type. The reduction will allow to describe the neuron interconnections in a quite large network and to obtain information concerning its symmetry and stability. Both cases, on homogeneous voltage across the membrane and inhomogeneous voltage along the axon will be tackled out. Few numerical simulations of the neural flow based on the cable-equation will be also presented.
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