書誌事項
- タイトル別名
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- A Hybrid Learning Algorithm Integrating Genetic Algorithms with Neural Networks for Saccade Generation Model
- サッケード ガンキュウ ウンドウ モデル ニ オケル イデンテキ アルゴリズム
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Saccade eye movements are among the most rapid yet precise of all movements produced by higher mammals. Recently we have proposed a spatio-temporal neural network model of the superior colliculus which uses lateral excitatory and inhibitory interconnections to help control both the dynamic and static behavior of saccadic eye movements. In this paper a new learning algorithm integrating genetic algorithms with neural networks for the lateral inhibitory and excitatory interconnections in the saccade generation model is presented. Data base for the training were obtained from neurophysiological experiments, and the training converged well even if random connections were chosen as initial conditions. The resulting network model succeeded in making accurate saccadic eye movements of a variety of sizes while producing realistic spatio-temporal patterns of collicular discharge.
収録刊行物
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- 電気学会論文誌Ｃ（電子・情報・システム部門誌）
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電気学会論文誌Ｃ（電子・情報・システム部門誌） 117 (2), 150-157, 1997
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390001204607715840
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- NII論文ID
- 130006843616
- 10004159989
- 10002809331
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 4128399
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- NDL
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