Attention modeled as information in learning multisensory integration

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. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).