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Related Experiment Video

Updated: Jun 2, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

A normalization model of multisensory integration.

Tomokazu Ohshiro¹, Dora E Angelaki, Gregory C DeAngelis

¹Department of Brain and Cognitive Sciences, Center for Visual Science, University of Rochester, Rochester, New York, USA.

Nature Neuroscience

|May 10, 2011

Summary

This summary is machine-generated.

Divisive normalization explains key principles of multisensory integration in neurons. This computational model accounts for empirical features and changing rules based on cue reliability.

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Area of Science:

Neuroscience
Computational Neuroscience
Sensory Processing

Background:

Multisensory integration by neurons is described by empirical principles.
A unifying computational framework for these principles is lacking.

Purpose of the Study:

To propose a computational framework for multisensory integration.
To demonstrate how divisive normalization can explain empirical principles of neural responses.

Main Methods:

Computational modeling of neural responses.
Applying the principle of divisive normalization to multisensory integration.

Main Results:

Divisive normalization accounts for the principle of inverse effectiveness and the spatial principle.
The model explains how cue reliability alters the mathematical rules of multisensory combination.
The model predicts cross-modal suppression.

Conclusions:

Divisive normalization offers a unifying computational account of multisensory integration.
This framework explains diverse empirical observations in single neurons.