Learning algorithm for saccade model with distributed feedback mechanism

Kuniharu Arai, Eitaro Aiyoshi

研究成果: Article

抜粋

In this paper we propose a new learning rule for a spatiotemporal neural network model of the primate saccadic system with a distributed feedback control mechanism. In our model the superior colliculus is represented as the distributed network model and it provides a dynamic control signal to a lumped brain stem model (Robinson-Gisbergen model). Distributed feedforward and feedback weights between the deeper layer of the superior colliculus model and the brain stem model are trained using a finite time interval learning rule based on a steepest descent method. Simulations are carried out on a 20-cell model for horizontal saccades using eye position feedback and velocity feedback, respectively. The model makes accurate saccades to all target locations over the range 2 to 15 degrees even if disturbance is added to the burst generator in the brain stem model.

元の言語English
ページ(範囲)66-76
ページ数11
ジャーナルElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
129
発行部数4
DOI
出版物ステータスPublished - 1999 12 1

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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