Learning algorithm for saccade model with distributed feedback mechanism

Kuniharu Arai, Eitaro Aiyoshi

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)66-76
Number of pages11
JournalElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
Volume129
Issue number4
DOIs
Publication statusPublished - 1999 Dec
Externally publishedYes

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Eye movements
Learning algorithms
Feedback
Brain
Steepest descent method
Feedback control
Neural networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Learning algorithm for saccade model with distributed feedback mechanism. / Arai, Kuniharu; Aiyoshi, Eitaro.

In: Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi), Vol. 129, No. 4, 12.1999, p. 66-76.

Research output: Contribution to journalArticle

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