creators_name: Harris, Harlan
creators_name: Reichler, Jesse
editors_name: Marko, Kenneth
editors_name: Werbos, Paul
type: confpaper
datestamp: 2002-07-03
lastmod: 2011-03-11 08:54:57
metadata_visibility: show
title: Learning in the Cerebellum with Sparse Conjunctions and Linear Separator Algorithms
ispublished: pub
subjects: comp-neuro-sci
subjects: neuro-mod
full_text_status: public
keywords: cerebellum, modeling, learning theory, winnow, idbd
abstract: This paper investigates potential learning rules 
in the cerebellum. We review evidence that input to the cerebellum is 
sparsely expanded by granule cells into a very wide basis vector, 
and that Purkinje
cells learn to compute a linear separation using that basis.
We review learning rules employed by existing cerebellar models, and show
that recent results from Computational Learning Theory suggest that
the standard delta rule would not be efficient.
We suggest that alternative, attribute-efficient learning rules, such as 
Winnow or Incremental Delta-Bar-Delta, are more appropriate for cerebellar
modeling, and support this position with results from a computational model.

date: 2001
date_type: published
publisher: IEEE
refereed: TRUE
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citation:   Harris, Harlan and Reichler, Jesse  (2001) Learning in the Cerebellum with Sparse Conjunctions and Linear Separator Algorithms.  [Conference Paper]     
document_url: http://cogprints.org/2310/2/sparsewinnow.ps