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，这是突出的特征之一，是用于空间信息的并行处理。理论上对阐明神经网络中并行处理的关键特征没有很好的讨论。在这项研究中，结果表明，不对称的非线性函数在移动检测的网络平行处理中起着至关重要的作用。视觉信息首先输入到视网膜神经网络中，然后将其传输到中继神经元，最后在皮质的视觉网络和大脑中的中间时间区域进行处理。在这些网络中，据报道，这种非线性功能将有效地处理视觉信息。我们明确指出，与均匀和奇数非线性函数的并行处理在运动检测中是有效的。运动检测的视觉皮层由两个分层网络组成，称为主要视觉皮层（V1），然后是中间时间区域（MT）。 V中的基本特征！通过分析不对称神经网络讨论了MT模型神经元。然后，将V1和MT模型网络分解为子不对称网络。通过优化不对称网络，得出了运动检测方程。然后，澄清说，V1和MT中均匀的非线性联合不对称网络在运动检测中至关重要。得出的结论是，从计算方面的角度，V1网络随后是MT网络，对运动信息进行了充分的处理。