title: Attention modeled as information in learning multisensory integration.
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.
authors: University of Hamburg, Department of Informatics, Knowledge Technology, WTM, Vogt-Kölln-Straße 30, 22527 Hamburg, Germany. Electronic address: bauer@informatik.uni-hamburg.de.
authors: Johannes
authors: J
authors: Bauer
authors: University of Hamburg, Department of Informatics, Knowledge Technology, WTM, Vogt-Kölln-Straße 30, 22527 Hamburg, Germany. Electronic address: magg@informatik.uni-hamburg.de.
authors: Sven
authors: S
authors: Magg
authors: University of Hamburg, Department of Informatics, Knowledge Technology, WTM, Vogt-Kölln-Straße 30, 22527 Hamburg, Germany. Electronic address: wermter@informatik.uni-hamburg.de.
authors: Stefan
authors: S
authors: Wermter
mesh_terms: D000818:Animals; D001288:Attention; D006801:Humans; D007858:Learning; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010465:Perception
keywords: Attention; Multisensory integration; Self-organization; Superior colliculus