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Shan Yu

Shan Yu, Ph. D

Professor

Brainnetome Center and National Laboratory of Pattern Recognition

Institute of Automation

Chinese Academy of Sciences

Beijing 100190, P. R. China

Email: shan.yu@nlpr.ia.ac.cn 


 Dr. Yu received his bachelor’s degree and doctor’s degree in biology from the University of Science and Technology of China in 2000 and 2005, respectively. From 2005-2014, he conducted postdoctoral research at the Max-Planck Institute of Brain Research in Frankfurt, Germany (2005-2008) and the National Institute of Mental Health in Bethesda, USA (2008-2014). He joined the Institute of Automation (IoA), Chinese Academy of Sciences (CAS) from Sep. 2014 as a recipient of the “One Hundred Talents” grant. Since then, he is a professor at the Brainnetome Center and National Laboratory of Pattern Recognition, IoA, CAS.

Research Interests 

1. Network mechanisms underlying neuronal information processing 

A fundamental challenge for systems neuroscience is to explain functions of the brain by studying neuronal activities. While a huge amount of knowledge has been accumulated regarding the behavior of single neurons, it is still far from clear how the brain achieves its remarkable feat as an organ of information processing, as well as how its functions are interrupted in various pathological conditions. To fill such an explanatory gap, we need to understand how numerous neurons, often with different response properties, can form an orchestrated network to perform computation, and how such computation is regulated according to the behavioral context. To develop such an intermediate-level description of neuronal information processing is my long-term goal. To this end, my research combines highly parallel electrophyiological recordings from macaque brain and advanced computational/theoretical approaches (e.g., network theory, information theory, statistical mechanics, etc) to investigate the structure and operation of neuronal networks, with focuses on understanding both how the brain works in normal conditions and the network mechanisms underlying major mental disorders. 

2. Brain-inspired computing 

The brain is a powerful machine for information processing, while capability of individual neurons seem to be rather limited. To understand how information processing emerges from interactions from relatively simple elements is a key to develop brain-inspired computing systems. Regarding emergent properties in complex systems, statistical physics provides by far the most important insights. Among them, the theory of criticality has been suggested to be highly relevant for understanding the brain. Critical systems exhibit a variety of functional advantages in terms of information storage, transmission and processing. At the same time, experimental brain research has provided ample evidence suggesting that the brain is operating at or close to a critical point. We would like to study the mechanisms underlying brain’s critical state and how such a state can facilitate computation in a complex, non-deterministic system. The aim is to translatethis knowledge to computational power in man-made systems. That is, to study how to exploit the functional benefits of a critical system to develop more powerful, flexible and efficient computing machine for practical applications. 


Publications

Huang X, Xu K, Chu C, Jiang T, Yu S. (2017) Weak Higher-Order Interactions in Macroscopic Functional Networks of the Resting Brain. Journal of Neuroscience 37 (43), 10481-10497

Yu S*, Ribeiro TL*, Meisel C, Chou S, Mitz A, Saunders R, Plenz D. (2017) Maintained avalanche dynamics during task-induced changes of neuronal activity in nonhuman primates. eLife 6:e27119

Yu S. (2016) New challenge for bionics—brain-inspired computing. Zoological Research 37 (5): 261

Fan L, Li H, Yu S, Jiang T. (2015) Human Brainnetome Atlas and Its Potential Applications in Brain-Inspired Computing. International Workshop on Brain-Inspired Computing, 1-14

Yu S*, Klaus K*, Yang H, Plenz D. (2014) Scale-invariant neuronal avalanche dynamics and the cut-off in size distributions. PloS one 9 (6), e99761

Yu S, Yang H, Shriki O, Plenz D. (2014) Critical exponents, universality class, and thermodynamic "temperature" for the brain. In Criticality in Neural Systems, edited by Plenz D and Nieber E. Wiley-VCH

Yu S*, Yang H*, Shriki O, Plenz D. (2013) Universal organization of resting brain activity at the thermodynamic critical point. Frontiers in Systems Neuroscience 7:42.

Larremore D, Shew W, Yu S, Plenz D, Ott E, Sorrentino F, Restrepo J. (2013) Inhibition guarantees ceaseless cortex network dynamics. preprint arXiv:1307.7658

Folias SE, Yu S, Snyder A, Nikolic D, Rubin JE. (2013) Synchronisation hubs in the visual cortex may arise from strong rhythmic inhibition during gamma oscillations. European Journal of Neuroscience 38 (6), 2864-2883

Fu Y*, Yu S*, Ma Y, Wang Y, Zhou Y. (2013) Functional degradation of the primary visual cortex during early senescence in rhesus monkeys. Cerebral Cortex 23(12): 2923-2931

Yu S, Yang H, Nakahara H, Santos, G.S, Nikolic D, Plenz D. (2011) Higher-order interactions characterized in cortical activities. Journal of Neuroscience 31(48):17514-17526

Yu S, Nikolic D. Quantum mechanics needs no consciousness. (2011) Annalen der Physik, 523(11): 931–938

Havenith MN, Yu S, Biederlack J, Chen N, Singer W, Nikolic D. (2011) Synchrony makes neurons fire in sequence, and stimulus properties determine who is ahead. Journal of Neuroscience 31(23):8570-8584

Shew WL, Yang H, Yu S, Roy R, Plenz D. (2011) Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches. Journal of Neuroscience 31(1):55-63.

Klaus A, Yu S, Plenz D. (2011) Statistical analyses support power law distributions found in neuronal avalanches. PLoS ONE 6(5):e19779

Jurjut OF, Nikolic D, Singer W, Yu S, Havenith MN, Mure?an RC. (2011) Timescales of multineuronal activity patterns reflect temporal structure of visual stimuli. PLoS ONE 6(2):e16758.

Hahn G, Petermann T, Havenith MN, Yu S, Singer W, Plenz D, Nikolic D. (2010) Neuronal avalanches in spontaneous activity in vivo. Journal of Neurophysiology 104(6):3312-22.

Havenith MN, Zemmar A, Yu S, Baudrexel SM, Singer W, Nikolic D. (2009) Measuring sub-millisecond delays in spiking activity with millisecond time-bins. Neuroscience Letters 450:296-300

Yu S, Huang D, Singer W, Nikolic D. (2008) A small world of neuronal synchrony. Cerebral Cortex 18(12):2891-2901

Yu S, Wang Y, Li X, Zhou Y, Leventhal AG. (2006) Functional degradation of extrastriate visual cortex in senescent rhesus monkeys. Neuroscience 140(3):1023-1029

Yu S, Wang XS, Fu Y, Zhang J, Ma YY, Wang YC, Zhou YF. (2005) Effects of age on latency and variability of visual response in monkeys. Chinese Science Bulletin 50(11):1163-1165

Liu N, Yu S, Zhou Y, Cai J, Ma Y. (2005) Age-related effects of bromocriptine on sensory gating in rhesus monkeys. Neuroreport 16(6):603-606

Yu S*, Liu N*, Zeng T, Tian S, Chen N, Zhou Y, Ma Y. (2004) Age-related effects of bilateral frontal eye fields lesions on rapid eye movements during REM sleep in rhesus monkeys. Neuroscience Letters 366(1):58-62

(* equal contribution)

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