Input minimization: A model of cerebellar learning without climbing fiber error signals

Research output: Contribution to journalArticlepeer-review

Abstract

The cerebellum is critical for motor learning. Current cerebellar learning models follow the Marr/Albus paradigm, in which climbing fibers provide error signals that shape plastic synapses between parallel fibers and Purkinje cells. However, climbing fibers have slow and largely random discharge, and seem unlikely to provide error signals with resolution sufficient to guide cerebellar learning. Parallel fibers carry error signals and could direct the plasticity of their own synapses, but the error signals are carried along with other signals. This report presents the new input minimization (InMin) model, in which Purkinie cells reduce error by minimizing their overall parallel fiber input. The slowly, randomly firing climbing fiber provides only synchronization pulses. InMin offers an alternative that can unify cerebellar findings.

Original languageEnglish (US)
Pages (from-to)3825-3831
Number of pages7
JournalNeuroreport
Volume12
Issue number17
DOIs
StatePublished - Dec 4 2001

Keywords

  • Cerebellum
  • Computational modeling
  • Learning algorithm
  • Motor learning
  • Plasticity
  • Reinforcement learning
  • Unsupervised learning

ASJC Scopus subject areas

  • Neuroscience(all)

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