Decentralized multisensory information integration in neural systems

Wen Hao Zhang, Aihua Chen, Malte J. Rasch, Si Wu

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

How multiple sensory cues are integrated in neural circuitry remains a challenge. The common hypothesis is that information integration might be accomplished in a dedicated multisensory integration area receiving feedforward inputs from the modalities. However, recent experimental evidence suggests that it is not a single multisensory brain area, but rather many multisensory brain areas that are simultaneously involved in the integration of information. Why many mutually connected areas should be needed for information integration is puzzling. Here, we investigated theoretically how information integration could be achieved in a distributed fashion within a network of interconnected multisensory areas. Using biologically realistic neural network models, we developed a decentralized information integration system that comprises multiple interconnected integration areas. Studying an example of combining visual and vestibular cues to infer heading direction, we show that such a decentralized system is in good agreement with anatomical evidence and experimental observations. In particular, we show that this decentralized system can integrate information optimally. The decentralized system predicts that optimally integrated information should emerge locally from the dynamics of the communication between brain areas and sheds new light on the interpretation of the connectivity between multisensory brain areas.

Original languageEnglish (US)
Pages (from-to)532-547
Number of pages16
JournalJournal of Neuroscience
Volume36
Issue number2
DOIs
StatePublished - Jan 13 2016
Externally publishedYes

Keywords

  • Continuous attractor neural network
  • Decentralized information integration

ASJC Scopus subject areas

  • Neuroscience(all)

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