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

对称与非对称网络结构神经网络认知与记忆机制的计算研究

基本信息

批准号：
15500134
负责人：
ISHII Naohiro
金额：
$ 2.37万
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：
2003
资助国家：
日本
起止时间：
2003 至 2004
项目状态：
已结题

项目摘要

In the neural networks, one of the prominent features, is a parallel processing for the spatial information. It is not well discussed theoretically to clarify the key features for the parallel processing in the neural networks. In this research, it is shown that the asymmetrical nonlinear functions play an crucial role in the network parallel processing for the movement detection. The visual information is inputted first to the retinal neural networks, then it is transmitted to the relay neurons and finally is processed in the visual network of the cortex and the middle temporal area in the brain. In these networks, it is reported that this kind of nonlinear functions will process the visual information effectively. We made clear that the parallel processing with the even and odd nonlinear functions, is 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 in V! and MT model neurons, are discussed by analyzing the asymmetric neural networks. Then, 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 even-odd nonlinearity combined asymmetric networks in the V1 and MT, are fundamental in the movement detection. It was concluded that the V1 network, followed by the MT network, process the movement information sufficiently from the view point of the computational aspects.

在神经网络中，一个突出的特点是对空间信息的并行处理。阐明神经网络中并行处理的关键特征在理论上还没有得到很好的讨论。研究表明，在运动检测的网络并行处理中，非对称的非线性函数起着至关重要的作用。视觉信息首先被输入到视网膜神经网络，然后被传输到中继神经元，最后在大脑皮质和中间颞区的视觉网络中进行处理。在这些网络中，有报道称这种非线性函数将有效地处理视觉信息。研究表明，奇偶非线性函数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，称为初级视觉皮质(V1)，随后是中间颞区(MT)。V！的基本特征！和MT模型神经元，通过对非对称神经网络的分析，对其进行了讨论。然后，将V1和MT模型网络分解为次不对称网络。通过对非对称网络的优化，推导出运动检测方程。阐明了V1和MT中的奇偶非线性组合非对称网络是运动检测的基础。从计算的角度来看，V1网络对运动信息的处理能力较强，MT网络次之。