Motor Learning with Unstable Neural Representations

Uri Rokni, Andrew G. Richardson, Emilio Bizzi, H. Sebastian Seung

Research output: Contribution to journalArticle

140 Scopus citations

Abstract

It is often assumed that learning takes place by changing an otherwise stable neural representation. To test this assumption, we studied changes in the directional tuning of primate motor cortical neurons during reaching movements performed in familiar and novel environments. During the familiar task, tuning curves exhibited slow random drift. During learning of the novel task, random drift was accompanied by systematic shifts of tuning curves. Our analysis suggests that motor learning is based on a surprisingly unstable neural representation. To explain these results, we propose that motor cortex is a redundant neural network, i.e., any single behavior can be realized by multiple configurations of synaptic strengths. We further hypothesize that synaptic modifications underlying learning contain a random component, which causes wandering among synaptic configurations with equivalent behaviors but different neural representations. We use a simple model to explore the implications of these assumptions.

Original languageEnglish (US)
Pages (from-to)653-666
Number of pages14
JournalNeuron
Volume54
Issue number4
DOIs
StatePublished - May 24 2007
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

Keywords

  • SYSBIO
  • SYSNEURO

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