A Normalization Model of Multisensory Integration
Identifieur interne : 002D50 ( Main/Exploration ); précédent : 002D49; suivant : 002D51A Normalization Model of Multisensory Integration
Auteurs : Tomokazu Ohshiro [États-Unis] ; Dora E. Angelaki [États-Unis] ; Gregory C. Deangelis [États-Unis]Source :
- Nature neuroscience [ 1097-6256 ] ; 2011.
Abstract
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 exhibited by single neurons, such as the principle of inverse effectiveness and the spatial principle. This model, which employs 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 key features of multisensory integration by neurons.
Url:
DOI: 10.1038/nn.2815
PubMed: 21552274
PubMed Central: 3102778
Affiliations:
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<front><div type="abstract" xml:lang="en"><p id="P1">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 exhibited by single neurons, such as the principle of inverse effectiveness and the spatial principle. This model, which employs 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 key features of multisensory integration by neurons.</p>
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<author><name sortKey="Deangelis, Gc" uniqKey="Deangelis G">GC DeAngelis</name>
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<author><name sortKey="Angelaki, De" uniqKey="Angelaki D">DE Angelaki</name>
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<affiliations><list><country><li>États-Unis</li>
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<region><li>Missouri (État)</li>
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<tree><country name="États-Unis"><region name="État de New York"><name sortKey="Ohshiro, Tomokazu" sort="Ohshiro, Tomokazu" uniqKey="Ohshiro T" first="Tomokazu" last="Ohshiro">Tomokazu Ohshiro</name>
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