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.

@Article{BMW15, 
 	 author =  {Bauer, Johannes and Magg, Sven and Wermter, Stefan},  
 	 title = {Attention modeled as information in learning multisensory integration}, 
 	 journal = {Neural Networks},
 	 number = {},
 	 volume = {65},
 	 pages = {44--52},
 	 year = {2015},
 	 month = {May},
 	 publisher = {Elsevier},
 	 doi = {10.1016/j.neunet.2015.01.004}, 
 }