Continuous attractors and oculomotor control

Research output: Contribution to journalArticle

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Abstract

A recurrent neural network can possess multiple stable states, a property that many brain theories have implicated in learning and memory. There is good evidence for such multistability in the brainstem neural network that controls eye position. Because the stable states are arranged in a continuous dynamical attractor, the network can store a memory of eye position with analog neural encoding. Continuous attractors in model networks depend on precisely tuned positive feedback, and their robust maintenance requires mechanisms of synaptic plasticity. These ideas may have wider scope than just the oculomotor system. More generally, the internal models postulated by theories of biological motor control may be recurrent networks with continuous attractors.

Original languageEnglish (US)
Pages (from-to)1253-1258
Number of pages6
JournalNeural Networks
Volume11
Issue number7-8
DOIs
StatePublished - Jan 1 1998
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence

Keywords

  • Continuous attractors
  • Internal models
  • Learning and memory
  • Motor control
  • Multistability
  • Positive feedback
  • Recurrent networks
  • Reverberating activity

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