---
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.
altloc: []
chapter: ~
commentary: ~
commref: ~
confdates: July 2001
conference: International Joint Conference on Neural Networks 2001
confloc: 'Washington, DC, USA'
contact_email: ~
creators_id: []
creators_name:
- family: Harris
given: Harlan
honourific: ''
lineage: ''
- family: Reichler
given: Jesse
honourific: ''
lineage: ''
date: 2001
date_type: published
datestamp: 2002-07-03
department: ~
dir: disk0/00/00/23/10
edit_lock_since: ~
edit_lock_until: ~
edit_lock_user: ~
editors_id: []
editors_name:
- family: Marko
given: Kenneth
honourific: ''
lineage: ''
- family: Werbos
given: Paul
honourific: ''
lineage: ''
eprint_status: archive
eprintid: 2310
fileinfo: /style/images/fileicons/application_postscript.png;/2310/2/sparsewinnow.ps
full_text_status: public
importid: ~
institution: ~
isbn: ~
ispublished: pub
issn: ~
item_issues_comment: []
item_issues_count: 0
item_issues_description: []
item_issues_id: []
item_issues_reported_by: []
item_issues_resolved_by: []
item_issues_status: []
item_issues_timestamp: []
item_issues_type: []
keywords: 'cerebellum, modeling, learning theory, winnow, idbd'
lastmod: 2011-03-11 08:54:57
latitude: ~
longitude: ~
metadata_visibility: show
note: ~
number: ~
pagerange: ~
pubdom: FALSE
publication: ~
publisher: IEEE
refereed: TRUE
referencetext: |+
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relation_type: []
relation_uri: []
reportno: ~
rev_number: 10
series: ~
source: ~
status_changed: 2007-09-12 16:44:07
subjects:
- comp-neuro-sci
- neuro-mod
succeeds: ~
suggestions: ~
sword_depositor: ~
sword_slug: ~
thesistype: ~
title: Learning in the Cerebellum with Sparse Conjunctions and Linear Separator Algorithms
type: confpaper
userid: 3218
volume: ~