Continuous Attractor Neural Networks: Candidate of a Canonical Model for Neural Information Representation

Si Wu, K. Y.Michael Wong, C. C.Alan Fung, Yuanyuan Mi, Wenhao Zhang

Research output: Contribution to journalReview articlepeer-review

18 Scopus citations

Abstract

Owing to its many computationally desirable properties, the model of continuous attractor neural networks (CANNs) has been successfully applied to describe the encoding of simple continuous features in neural systems, such as orientation, moving direction, head direction, and spatial location of objects. Recent experimental and computational studies revealed that complex features of external inputs may also be encoded by low-dimensional CANNs embedded in the high-dimensional space of neural population activity. The new experimental data also confirmed the existence of the M-shaped correlation between neuronal responses, which is a correlation structure associated with the unique dynamics of CANNs. This body of evidence, which is reviewed in this report, suggests that CANNs may serve as a canonical model for neural information representation.

Original languageEnglish (US)
Article number156
JournalF1000Research
Volume5
DOIs
StatePublished - 2016
Externally publishedYes

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

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