Linking neurons to behavior in multisensory perception: A computational review

Wei Ji Ma, Alexandre Pouget

Research output: Contribution to journalReview articlepeer-review


A large body of psychophysical and physiological findings has characterized how information is integrated across multiple senses. This work has focused on two major issues: how do we integrate information, and when do we integrate, i.e., how do we decide if two signals come from the same source or different sources. Recent studies suggest that humans and animals use Bayesian strategies to solve both problems. With regard to how to integrate, computational studies have also started to shed light on the neural basis of this Bayes-optimal computation, suggesting that, if neuronal variability is Poisson-like, a simple linear combination of population activity is all that is required for optimality. We review both sets of developments, which together lay out a path towards a complete neural theory of multisensory perception.

Original languageEnglish (US)
Pages (from-to)4-12
Number of pages9
JournalBrain Research
StatePublished - Nov 25 2008


  • Computational modeling
  • Cue combination
  • Multisensory integration
  • Neural variability
  • Population coding
  • Segregation
  • Superadditivity

ASJC Scopus subject areas

  • General Neuroscience
  • Molecular Biology
  • Clinical Neurology
  • Developmental Biology


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