Titlebook: Dynamic Interactions in Neural Networks: Models and Data; Michael A. Arbib,Shun-ichi Amari Conference proceedings 1989 Springer-Verlag New

Wilson

書目名稱Dynamic Interactions in Neural Networks: Models and Data影響因子(影響力)




書目名稱Dynamic Interactions in Neural Networks: Models and Data影響因子(影響力)學科排名




書目名稱Dynamic Interactions in Neural Networks: Models and Data網絡公開度




書目名稱Dynamic Interactions in Neural Networks: Models and Data網絡公開度學科排名




書目名稱Dynamic Interactions in Neural Networks: Models and Data被引頻次




書目名稱Dynamic Interactions in Neural Networks: Models and Data被引頻次學科排名




書目名稱Dynamic Interactions in Neural Networks: Models and Data年度引用




書目名稱Dynamic Interactions in Neural Networks: Models and Data年度引用學科排名




書目名稱Dynamic Interactions in Neural Networks: Models and Data讀者反饋




書目名稱Dynamic Interactions in Neural Networks: Models and Data讀者反饋學科排名

AVOW

A Model for Oblique Saccade Generation and Adaptationhigh-gain servo system by placing a deadband element in a high-gain forward path. In spite of logical completeness, the desirable position signals or feedback positional error signals cannot be found in the actual brain stem circuitry, as was expected from the model.

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NOMAD

GLIDE

Qiangze Feng,Hongwei Qi,Toshikazu Fukushima These effects of attention may explain both why we have little awareness of unattended stimuli, and why our resolution of spatial location and visual features is improved inside the focus of attention.

GLIDE

Compressing XML Data Streams with DAG+BSBCand it seems urgent to solve the controversy on a sound experimental basis. One clue for solution may be obtained from examination of involvment of the inferior olive in the VOR adaptation, as the inferior olive has been assumed to play a key role in learning mechanisms of the cerebellar cortex.

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虛情假意

Conference proceedings 1989ation processing models of living brains - and in neural computing - the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume, Dynamic Interactions in Neural Networks: Models and Data can be given two interpretations. We present models and da

Goblet-Cells