A normalization model of multisensory integration

Tomokazu Ohshiro, Dora E. Angelaki, Gregory C. Deangelis

Research output: Contribution to journalArticlepeer-review


Responses of neurons that integrate multiple sensory inputs are traditionally characterized in terms of a set of empirical principles. However, a simple computational framework that accounts for these empirical features of multisensory integration has not been established. We propose that divisive normalization, acting at the stage of multisensory integration, can account for many of the empirical principles of multisensory integration shown by single neurons, such as the principle of inverse effectiveness and the spatial principle. This model, which uses a simple functional operation (normalization) for which there is considerable experimental support, also accounts for the recent observation that the mathematical rule by which multisensory neurons combine their inputs changes with cue reliability. The normalization model, which makes a strong testable prediction regarding cross-modal suppression, may therefore provide a simple unifying computational account of the important features of multisensory integration by neurons.

Original languageEnglish (US)
Pages (from-to)775-782
Number of pages8
JournalNature Neuroscience
Issue number6
StatePublished - Jun 2011

ASJC Scopus subject areas

  • General Neuroscience


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