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Brainnetome Lecture Series - Concurrent Multisensory Integration and Segregation in a Decentralized Network in the Brain

  Title: Concurrent Multisensory Integration and Segregation in a Decentralized Network in the Brain 

Speaker: Dr. Wen-Hao Zhang, University of Pittsburgh, USA 

Chair: Prof. Shan Yu, Brainnetome Center, CASIA 

Time: 09:30-10:30, Dec. 6, 2018 

Venue: The 3rd meeting room, 3rd floor, Intelligence Building 


Our brain perceives the world by exploiting multiple sensory modalities to extract information about various aspects of external stimuli. Extensive studies indicate that the information processing in the brain is implementing probabilistic inference: the network of neurons compute the posterior belief over unobserved causes given observations. In multisensory processing, if sensory inputs are from the same stimulus of interest, they should be integrated to improve perception; otherwise, they should be segregated to distinguish different stimuli. In reality, however, the brain faces the challenge of recognizing stimuli without knowing in advance whether sensory inputs come from the same or different stimuli. How multisensory processing is implemented in neural circuitry remains a challenge. In this talk, I would like to present my recent effort in pursuing this question through combining theory and experimental evidence. Experiments suggest that there are several multisensory brain areas that are simultaneously involved in multisensory integration. And within each brain area, there are two distinguishing groups of neurons, congruent neurons which exhibit integrative responses and opposite neurons whose functions remain mystery yet. Inspired by experiments, we propose a decentralized network composed of several inter-connected modules, with each module modelling a multisensory brain area. In our model, the multisensory integration is achieved by the cross-talk between congruent neurons across network modules, while opposite neurons compute the disparity between multisensory inputs. And eventually the concurrent integration and segregation emerge locally from the interplay between two types of neurons across network modules. Through this process, the brain achieves rapid stimulus perception if the inputs come from the same stimulus of interest, and differentiates and recognizes stimuli based on individual input with little time delay if the they come from different stimuli of interest. Our study may shed new light on a new architecture for multisensory processing in artificial intelligence.
Dr. Wen-hao Zhang is a postdoc researcher conducting theoretical neuroscience research in Department of Mathematics, University of Pittsburgh. He got his PhD at Institute of Neuroscience, Chinese Academy of Sciences on 2015. Then he worked in Department of Physics at Hong Kong University of Science and Technology during 2015-2016, and Carnegie Mellon University during 2016-2017. His current researches primarily focus on investigating the biologically plausible network implementation of Bayesian inference. Besides, he is also interested in nonlinear network dynamics, neural coding and synaptic plasticity.