2001 Fiscal Year Final Research Report Summary
Computational Study on Recognition and Memory in the Asymmetric <symmetric Neural Networks
| Project/Area Number |
12680379
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Nagoya Institute of Technology |
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Principal Investigator |
ISHII Naohiro Nagoya Inst. Tech., Intell. & Comp., Professor, 工学部, 教授 (50004619)
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| Co-Investigator(Kenkyū-buntansha) |
YAMAUCHI Koichiro Hokkaidou Univ., Associate Professor, 工学研究科, 助教授 (00262949)
IWAHORI Yuji Nagoya Inst. Tech., Media Center, Professor, 工学部, 教授 (60203402)
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| Project Period (FY) |
2000 – 2001
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| Keywords | Neural Network / Asymmetric network / Nonlinear Analysis / Construction of two asymmetric network / Moving stimulus / Retinal networks / Higher-order correlation / Optimal pathway |
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| Research Abstract |
Biological neural networks are different from artificial neural networks. There are functionally different neurons in the networks. In the field of neuro-physiology , neurons are classified into cells with several types of function by the experiments. We classify the biological neural network morphologically into two types of sub-networks. One is the symmetrical network, while the other is the asymmetrical network. We extended this asymmetrical network to the generalized asymmetrical network to clarify the characteristics of the asymmetrical networks. The generalized asymmetrical network extended to the parallel network with the odd nonlinear pathway of neurons and the even nonlinear pathway of neurons. The function of this generalized asymmetrical networks, is analyzed by Wiener analysis method. Then, the temporal correlations are important in the asymmetrical network. We derived this generalized network is reduced to the asymmetrical network, which consists of the parallel network with the linear pathway, which shows an odd nonlinearity, and with the nonlinear pathway, which shows the 2nd order nonlinearity. We clarified that the biological network is composed with two asymmetrical sub-networks, which realizes the spacial and temporal correlation as the stimulus information transmitted to the central neural network.
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