Attractor Network Models

Research output: Chapter in Book/Report/Conference proceedingEntry for encyclopedia/dictionary

Abstract

The term attractor, when applied to neural circuits, refers to dynamical states of neural populations that are self-sustained and stable against perturbations. It is part of the vocabulary for describing neurons or neural networks as dynamical systems. This concept helps to quantitatively describe self-organized spatiotemporal neuronal firing patterns in a circuit, during spontaneous activity or underlying brain functions. Moreover, the theory of dynamical systems provides tools for examining the stability and robustness of a neural circuit's behavior, proposes a theory of learning and memory in terms of the formation of multiple attractor states or continuous attractors, and provides insights into how variations in cellular/synaptic properties give rise to a diversity of computational capabilities.

Original languageEnglish (US)
Title of host publicationEncyclopedia of Neuroscience
PublisherElsevier Ltd
Pages667-679
Number of pages13
ISBN (Print)9780080450469
DOIs
StatePublished - Jan 1 2009

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Keywords

  • Associative memory
  • Continuous attractor
  • Decision making
  • Dynamic system
  • Molecular switch
  • Multistability
  • Neuronal spatiotemporal firing patterns
  • Persistent activity
  • Prefrontal cortex
  • Spontaneous up and down states
  • Time integration
  • Working memory

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Wang, X. J. (2009). Attractor Network Models. In Encyclopedia of Neuroscience (pp. 667-679). Elsevier Ltd. https://doi.org/10.1016/B978-008045046-9.01397-8