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A model of smooth pursuit based on learning of the target dynamics using only retinal signals

2005

Article

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While the predictive nature of the primate smooth pursuit system has been evident through several behavioural and neurophysiological experiments, few models have attempted to explain these results comprehensively. The model we propose in this paper in line with previous models employing optimal control theory; however, we hypothesize two new issues: (1) the medical superior temporal (MST) area in the cerebral cortex implements a recurrent neural network (RNN) in order to predict the current or future target velocity, and (2) a forward model of the target motion is acquired by on-line learning. We use stimulation studies to demonstrate how our new model supports these hypotheses.

Author(s): Shibata, T. and Tabata, H. and Schaal, S. and Kawato, M.
Book Title: Neural Networks
Volume: 18
Pages: 213-225
Year: 2005

Department(s): Autonomous Motion
Bibtex Type: Article (article)

Cross Ref: p1595
Note: clmc
URL: http://www-clmc.usc.edu/publications/S/shibata-NN2005.pdf

BibTex

@article{Shibata_NN_2005,
  title = {A model of smooth pursuit based on learning of the target dynamics using only retinal signals},
  author = {Shibata, T. and Tabata, H. and Schaal, S. and Kawato, M.},
  booktitle = {Neural Networks},
  volume = {18},
  pages = {213-225},
  year = {2005},
  note = {clmc},
  crossref = {p1595},
  url = {http://www-clmc.usc.edu/publications/S/shibata-NN2005.pdf}
}