Show Reference: "Learning Multi-Sensory Integration with Neural Self-Organization and Statistics"

Learning Multi-Sensory Integration with Self-Organization and Statistics In Ninth International Workshop on Neural-Symbolic Learning and Reasoning (NeSy'13) (August 2013), pp. 7-12 by Johannes Bauer, Stefan Wermter edited by Artur Garcez, Luis Lamb, Pascal Hitzler
@inproceedings{bauer-and-wermter-2013b,
    address = {Beijing, China},
    author = {Bauer, Johannes and Wermter, Stefan},
    booktitle = {Ninth International Workshop on Neural-Symbolic Learning and Reasoning (NeSy'13)},
    editor = {Garcez, Artur and Lamb, Luis and Hitzler, Pascal},
    keywords = {ann, learning, model, multisensory-integration, sc, som, unsupervised-learning},
    location = {Beijing, China},
    month = aug,
    pages = {7--12},
    posted-at = {2013-08-03 10:19:08},
    priority = {2},
    title = {Learning {Multi-Sensory} Integration with {Self-Organization} and Statistics},
    url = {http://knoesis.wright.edu/faculty/pascal/nesy/NeSy13/},
    year = {2013}
}

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Yay! I'm bridging that gulf as well!

My model is normative, performs optimally and it shows super-additivity (to be shown).

Bauer and Wermter use the algorithm they proposed to model multi-sensory integration in the SC. They show that it can learn to near-optimally integrate noisy multi-sensory information and reproduces spatial register of sensory maps, the spatial principle, the principle of inverse effectiveness, and near-optimal audio-visual integration in object localization.