Computational Research on Cognition and Memory Mechanism in Neural Networks with Asymmetric and Symmetric Network Structures

非对称和对称网络结构神经网络认知记忆机制的计算研究

基本信息

批准号：
17500154
负责人：
ISHII Naohiro
金额：
$ 2.24万
依托单位：
Aichi Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2006
项目状态：
已结题

项目摘要

In the neural networks, a parallel processing with nonlinear characteristics for the spatial information will be a fundamental principle of the visual information processing. It is not well discussed theoretically to clarify the key features for the parallel processing with nonlinear characteristics in the neural networks. In this research, it is shown that the asymmetrical nonlinear functions in the cascaded network with two network layers, play an crucial role in the parallel processing in the movement detection. Then, an ensemble processing of the parallel processing, is newly developed here. We made clear that the parallel processing with the even and odd nonlinear functions, is an effective in the-movement detection. The visual cortex for the movement detection, consists of two layered networks, called the primary visual cortex (V1), followed by the middle temporal area (MT). The fundamental characteristics of the neural network structure in V1 and MT of the visual cortex, is an asymmetrical network with a nonlinear pathway and a linear pathway The model neurons, clarified by Prof Naka at New York York University are discussed by analyzing the asymmetric neural networks. Then the analysis method was applied to the visual cortex networks. The V1 and MT model networks, are decomposed into sub-asymmetrical networks. By the optimization of the asymmetric networks, the movement detection equations are derived. Then, it was clarified that the ensemble parallel processing with the even-odd nonlinearity combined asymmetric networks in the V1 and MT, are fundamental in the movement detection. It was concluded that the ensemble parallel processing of V1 network, followed by the MT network, process the movement information sufficiently from the view point of the computational aspects. The ensemble processing developed here was applied to the text mining problems

在神经网络中，对空间信息具有非线性特性的并行处理将成为视觉信息处理的基本原理。阐明神经网络中具有非线性特性的并行处理的关键特征还没有得到很好的理论讨论。本研究表明，具有两个网络层的级联网络中的不对称非线性函数在运动检测的并行处理中发挥着至关重要的作用。然后，这里新开发了并行处理的集成处理。我们明确了偶数和奇数非线性函数的并行处理在运动检测中是有效的。用于运动检测的视觉皮层由两层网络组成，称为初级视觉皮层（V1），其次是中颞区（MT）。视觉皮层V1和MT的神经网络结构的基本特征是具有非线性通路和线性通路的不对称网络。通过分析不对称神经网络，讨论了纽约大学Naka教授阐明的模型神经元。然后将该分析方法应用于视觉皮层网络。 V1 和 MT 模型网络被分解为子非对称网络。通过对非对称网络的优化，推导出运动检测方程。然后，阐明了 V1 和 MT 中奇偶非线性组合非对称网络的集成并行处理是运动检测的基础。结论是，从计算方面的角度来看，V1 网络的集成并行处理以及随后的 MT 网络可以充分处理运动信息。这里开发的集成处理应用于文本挖掘问题