Expectation Learning for Adaptive Crossmodal Stimuli Association

EUCog Meeting Proceedings doi: ARXIV:1801.07654 - Nov 2017 Open Access
Associated documents :  
The human brain is able to learn, generalize, and predict crossmodal stimuli. Learning by expectation fine-tunes crossmodal processing at different levels, thus enhancing our power of generalization and adaptation in highly dynamic environments. In this paper, we propose a deep neural architecture trained by using expectation learning accounting for unsupervised learning tasks. Our learning model exhibits a self-adaptable behavior, setting the first steps towards the development of deep learning architectures for crossmodal stimuli association.


 	 author =  {Barros, Pablo and Parisi, German I. and Fu, Di and Liu, Xun and Wermter, Stefan},  
 	 title = {Expectation Learning for Adaptive Crossmodal Stimuli Association}, 
 	 booktitle = {EUCog Meeting Proceedings},
 	 number = {},
 	 volume = {},
 	 pages = {},
 	 year = {2017},
 	 month = {Nov},
 	 publisher = {EUCog Meeting},
 	 doi = {ARXIV:1801.07654},