Hierarchical Learning of Voluntary Movement by Cerebellum and Sensory Association Cortex View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1989

AUTHORS

Mitsuo Kawato , Michiaki Isobe , Ryoji Suzuki

ABSTRACT

In earlier papers, we have proposed the feedback-error-learning of inverse dynamics model of the musculoskeletal system as heterosynaptic learning scheme in the cerebrocerebellum and the parvocellular part of the red nucleus system, and the iterative learning in the parietal association cortex. In this paper, we applied hierarchical arrangement of these two neural network models to learning trajectory control of an industrial robotic manipulator. We found that the hierarchical arrangement of the cerebellar and cerebral neural networks not only increased control stability but also dramatically improved accuracy of control and reduced required learning time. More... »

PAGES

195-214

References to SciGraph publications

Book

TITLE

Dynamic Interactions in Neural Networks: Models and Data

ISBN

978-0-387-96893-3
978-1-4612-4536-0

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4612-4536-0_12

DOI

http://dx.doi.org/10.1007/978-1-4612-4536-0_12

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049837635


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