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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, attributeefficient learning rules, such as
Winnow or Incremental DeltaBarDelta, are more appropriate for cerebellar
modeling, and support this position with results from a computational model.

Harris
Harlan

Reichler
Jesse
July 2001
International Joint Conference on Neural Networks 2001
Washington, DC, USA

Marko
Kenneth

Werbos
Paul
pub
cerebellum, modeling, learning theory, winnow, idbd
FALSE
IEEE
TRUE
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Learning in the Cerebellum with Sparse Conjunctions and Linear Separator Algorithms
published
2001
public