# Show Reference: "A Self-Organized Artificial Neural Network Architecture for Sensory Integration with Applications to Letter-Phoneme Integration"

A Self-Organized Artificial Neural Network Architecture for Sensory Integration with Applications to Letter-Phoneme Integration Neural Computation, Vol. 23, No. 8. (26 April 2011), pp. 2101-2139, doi:10.1162/neco_a_00149 by Tamas Jantvik, Lennart Gustafsson, Andrew P. Papliński
@article{jantvik-et-al-2011,
abstract = {The multimodal self-organizing network ({MMSON}), an artificial neural network architecture carrying out sensory integration, is presented here. The architecture is designed using neurophysiological findings and imaging studies that pertain to sensory integration and consists of interconnected lattices of artificial neurons. In this artificial neural architecture, the degree of recognition of stimuli, that is, the perceived reliability of stimuli in the various subnetworks, is included in the computation. The {MMSON}'s behavior is compared to aspects of brain function that deal with sensory integration. According to human behavioral studies, integration of signals from sensory receptors of different modalities enhances perception of objects and events and also reduces time to detection. In neocortex, integration takes place in bimodal and multimodal association areas and result, not only in feedback-mediated enhanced unimodal perception and shortened reaction time, but also in robust bimodal or multimodal percepts. Simulation data from the presented artificial neural network architecture show that it replicates these important psychological and neuroscientific characteristics of sensory integration. The multimodal self-organizing network ({MMSON}), an artificial neural network architecture carrying out sensory integration, is presented here. The architecture is designed using neurophysiological findings and imaging studies that pertain to sensory integration and consists of interconnected lattices of artificial neurons. In this artificial neural architecture, the degree of recognition of stimuli, that is, the perceived reliability of stimuli in the various subnetworks, is included in the computation. The {MMSON}'s behavior is compared to aspects of brain function that deal with sensory integration. According to human behavioral studies, integration of signals from sensory receptors of different modalities enhances perception of objects and events and also reduces time to detection. In neocortex, integration takes place in bimodal and multimodal association areas and result, not only in feedback-mediated enhanced unimodal perception and shortened reaction time, but also in robust bimodal or multimodal percepts. Simulation data from the presented artificial neural network architecture show that it replicates these important psychological and neuroscientific characteristics of sensory integration.},
author = {Jantvik, Tamas and Gustafsson, Lennart and Papli\'{n}ski, Andrew P.},
day = {26},
doi = {10.1162/neco\_a\_00149},
journal = {Neural Computation},
keywords = {ann, multi-modality, som},
month = apr,
number = {8},
pages = {2101--2139},
posted-at = {2011-09-27 09:31:32},
priority = {2},
publisher = {MIT Press},
title = {A {Self-Organized} Artificial Neural Network Architecture for Sensory Integration with Applications to {Letter-Phoneme} Integration},
url = {http://dx.doi.org/10.1162/neco\_a\_00149},
volume = {23},
year = {2011}
}