Attention modeled as information in learning multisensory integration. (May 2015)

• Record Type: Journal Article
• Authors: Bauer, Johannes; Magg, Sven; Wermter, Stefan
• Is Part Of: Neural networks. Volume 65(2015:May)
• Page Start: 44
• Page End: 52
• Publication Date: 2015-05
• Subjects: Attention -- Multisensory integration -- Superior colliculus -- Self-organization; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2015.01.004
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Top-down cognitive processes affect the way bottom-up cross-sensory stimuli are integrated. In this paper, we therefore extend a successful previous neural network model of learning multisensory integration in the superior colliculus (SC) by top-down, attentional input and train it on different classes of cross-modal stimuli. The network not only learns to integrate cross-modal stimuli, but the model also reproduces neurons specializing in different combinations of modalities as well as behavioral and neurophysiological phenomena associated with spatial and feature-based attention. Importantly, we do not provide the model with any information about which input neurons are sensory and which are attentional. If the basic mechanisms of our model–self-organized learning of input statistics and divisive normalization–play a major role in the ontogenesis of the SC, then this work shows that these mechanisms suffice to explain a wide range of aspects both of bottom-up multisensory integration and the top-down influence on multisensory integration.