# Show Reference: "A normalization model of multisensory integration"

A normalization model of multisensory integration Nature Neuroscience, Vol. 14, No. 6. (June 2011), pp. 775-782, doi:10.1038/nn.2815 by Tomokazu Ohshiro, Dora E. Angelaki, Gregory C. DeAngelis
@article{ohshiro-et-al-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 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.},
author = {Ohshiro, Tomokazu and Angelaki, Dora E. and DeAngelis, Gregory C.},
doi = {10.1038/nn.2815},
issn = {1546-1726},
journal = {Nature Neuroscience},
keywords = {computational, cue-combination, divisive-normalization, model, multi-modality, suppression},
month = jun,
number = {6},
pages = {775--782},
pmcid = {PMC3102778},
pmid = {21552274},
posted-at = {2012-07-04 11:00:29},
priority = {2},
publisher = {Nature Publishing Group},
title = {A normalization model of multisensory integration},
url = {http://dx.doi.org/10.1038/nn.2815},
volume = {14},
year = {2011}
}


Fetsch et al. acknowledge the similarity of their model with that of Ohshiro et al.

Fetsch et al. provide some sort of normative motivation to the model due to Ohshiro et al.

The ANN model of multi-sensory integration in the SC due to Ohshiro et al. manages to replicate a number of physiological finding about the SC:

• inverse effectiveness,
• long-range inhibition and
• short-range activation,
• multisensory integration,
• different tuning to modalities between neurons,
• weighting of stimuli from different modalities.

It does not learn and it has no probabilistic motivation.

The ANN model of multi-sensory integration in the SC due to Ohshiro et al. uses divisive normalization to model multisensory integration in the SC.