2004 Fiscal Year Final Research Report Summary
Computational Research on Cognition and Memory Mechanism in Neural Networks with Symmetric and Asymmetric Network Structures
| Project/Area Number |
15500134
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Sensitivity informatics/Soft computing
|
|---|
| Research Institution | Aichi Institute of Technology |
|---|
Principal Investigator |
ISHII Naohiro Aichi Institute of Technology, Department of Business and Information Science, Professor, 経営情報科学部, 教授 (50004619)
|
|---|
| Project Period (FY) |
2003 – 2004
|
| Keywords | asymmetric structure / nonlinear neural network / asymmetric and nonlinear network / Wiener analysis / movement detection / visual cortex V1 and MT / even and odd nonlinearities / optimization of networks |
|---|
| Research Abstract |
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.
|
Research Products
(17 results)
