The 'Neural' Phonetic Typewriter
Computer
Bayesian decision theory and psychophysics
Perception as Bayesian inference
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Speech and Audio Signal Processing: Processing and Perception of Speech and Music
Speech and Audio Signal Processing: Processing and Perception of Speech and Music
Self-Organizing Maps
Tutorial on maximum likelihood estimation
Journal of Mathematical Psychology
Early lexical development in a self-organizing neural network
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Reading Speech from Still and Moving Faces: The Neural Substrates of Visible Speech
Journal of Cognitive Neuroscience
Neural Specialization for Letter Recognition
Journal of Cognitive Neuroscience
The Fusiform "Face Area" is Part of a Network that Processes Faces at the Individual Level
Journal of Cognitive Neuroscience
Visual processing affects the neural basis of auditory discrimination
Journal of Cognitive Neuroscience
A multimodal self-organizing network for sensory integration of letters and phonemes
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
Multimodal feedforward self-organizing maps
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Feedback in multimodal self-organizing networks enhances perception of corrupted stimuli
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
A recurrent multimodal network for binding written words and sensory-based semantics into concepts
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Incremental self-organizing map (iSOM) in categorization of visual objects
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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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.