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Continuous attractors and oculomotor control.

H Sebastian Seung1

  • 1Bell Laboratories, Lucent Technologies, Murray Hill, USA

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2003
PubMed
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Recurrent neural networks exhibit multistability, crucial for learning and memory. This property, observed in brainstem networks controlling eye position, allows for analog memory storage via continuous attractors.

Area of Science:

  • Computational Neuroscience
  • Neuroscience
  • Systems Neuroscience

Background:

  • Recurrent neural networks (RNNs) can exhibit multiple stable states, a property linked to learning and memory in brain theories.
  • The brainstem neural network controlling eye position shows evidence of multistability.
  • Continuous dynamical attractors enable analog neural encoding for memory storage.

Purpose of the Study:

  • To explore the role of multistability and continuous attractors in neural networks.
  • To investigate the mechanisms underlying memory storage in the oculomotor system.
  • To assess the broader applicability of these concepts in biological motor control.

Main Methods:

  • Analysis of recurrent neural network dynamics.
  • Modeling of brainstem neural networks.

Related Experiment Videos

  • Investigation of positive feedback and synaptic plasticity mechanisms.

    • Main Results:

    • Demonstrated that continuous attractors in RNNs depend on tuned positive feedback.
    • Highlighted the necessity of synaptic plasticity for robust attractor maintenance.
    • Provided evidence for analog neural encoding of eye position memory.

    Conclusions:

    • Multistability in RNNs is a key mechanism for learning and memory.
    • Continuous attractors offer a model for analog memory in neural systems.
    • These principles may extend to internal models in broader biological motor control theories.