2018.11.19 What is Computational neuroscience?

2019-07-07 00:59:11 1








What is Computational neuroscience?

Xiao-Jing Wang (New York University, Shanghai Research Center for Brain Science and Brain-inspired Intelligence)

Abstract: I will introduce the cross-disciplinary field of theoretical/computational neuroscience, and its recent opportunities in China. To illustrate the field by examples, I will discuss strongly recurrent neural network models for elemental cognitive functions such as decision-making (how the brain makes a risky choice among several options based on expected outcomes). Moreover, I will argue that we are entering a new era of computational neuroscience, in close interplay with experimental advances, for understanding multi-regional large-scale brain circuits bridging neuroscience with artificial intelligence and psychiatry.


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2.      汪小京 (2010) “21世纪的中国计算神经科学展望 《科学时报》(Science Times, 2010年八月25. http://news.sciencenet.cn/sbhtmlnews/2010/8/235983.html?id=235983

3.      汪小京 (2015) “专家谈计算神经科学与类脑人工智能的关系,科学网,2015815日。http://news.sciencenet.cn/htmlnews/2015/8/324932.shtm

4.      汪小京 (2015) “脑科学需要自己的牛顿 ,科学网,20151124日。http://news.sciencenet.cn/htmlnews/2015/11/332429.shtm

5.      Wang X-J (2008) Decision making in recurrent neural circuits. Neuron 60, 215-234.

6.      Wang X-J and Krystal J (2014) Computational Psychiatry. Neuron 84, 638-654.



Exploring the functional relevance of feedback projections in our brain

Bin Min (Shanghai Research Center for Brain Science and Brain-inspired Intelligence)

Abstract: The hierarchical organization of the brain's ventral visual pathway has inspired the feedforward connectionist architectures used in state-of-the-art deep learning methods that have begun to transform applications as diverse as image recognition, disease diagnosis and self-driving cars.  However, it is well-known that there are way more feedback projections than feedforward ones in the brain. The function of these feedback projections remains an open question in neuroscience. In this talk, I will talk about our recent result about categorical perception, a hypothesis regarding how feedback projections can provide abstract category knowledge that is able to alter our sensory perception. According to this hypothesis, category learning would warp our perception such that differences between objects that belong to different categories are exaggerated (expansion) while differences within the same category are deemphasized (compression). This suggests a top-down influence from category-selective to feature-selective representations, but the underlying neural mechanisms have not been established. To gain insight into this question, we examined data from behavioral categorization experiments in non-human primates. In the experiments, monkeys performed the same visual motion discrimination task before and after visual motion categorization training. Data analysis shows that, after categorization training, stimuli within the same category were more difficult to discriminate than before categorization training, while the change for stimuli that belong to different categories was less pronounced, supporting compression without clear expansion. To explain this result, we built a neural circuit model that incorporates key existing experimental findings and makes new predictions, including: (1) learned categories are encoded in the spiking activities of neurons in the lateral intraparietal (LIP) area, (2) neurons in the middle temporal area show graded encoding of stimulus motion directions and (3) neurons in the medial superior temporal (MST) area integrate top-down category and bottom-up motion direction information. This model proposes that it is mainly through the feedback projections from LIP to MST that learned categories induce categorical perception. We find that this prediction is largely consistent with recent single neuron recordings in the MST and LIP areas. Collectively, we show the first behavioral evidence for compression in a visual motion discrimination task in non-human primates and develop a biological neural circuit model that allows us to make experimentally testable predictions, thereby elucidating the possible underlying neural mechanisms of categorical perception.


Xiao-Jing Wang is Distinguished Global Professor of Neural Science, director of the Swartz Center for Theoretical Neuroscience at New York University. In 2012-2017, he served as the founding Provost and Associate Vice Chancellor for Research at NYU Shanghai. Prior to joining NYU in the fall of 2012, Wang was Professor of Neurobiology at Yale University. Wang is an expert in Theoretical and Computational Neuroscience, with a special interest in the neurobiology of executive and cognitive functions. His group has pioneered neural circuit theory of the prefrontal cortex, which is often called the “CEO of the brain”. In recent years, his research group has been developing biologically-realistic large-scale brain circuit models, with the goal to elucidate the complex global brain mechanisms of cognitive functions flexible behavior as well as applications to artificial intelligence and Psychiatry. Wang is a Fellow of the American Association for the Advancement of Sciencea recipient of Alfred P. Sloan Research Fellowship, National Science Foundation CAREER Award, the Swartz Prize for Theoretical and Computational Neuroscience Prize, and the Goldman-Rakic Prize for Outstanding Achievement in Cognitive Neuroscience. He also received the one thousand talent award from the Chinese government.



Bin Min obtained his Bachelor degree in Applied Mathematics from Peking University in 2008, then received his Ph.D. in computational mathematics also from Peking University in 2013 when he switched to the field of computational neuroscience. In 2013-2018, he performed his postdoctoral research first at Courant Institute of Mathematical Science and then at Center for Neural Science, both of which are at New York University. In September of 2018, he joined the new brain institute in Shanghai --- Shanghai Research Institute for Brain Science and Brain-Inspired Intelligence. His main research interest is to understand the functional relevance of feedback projections in the brain, with the current working hypotheses that feedback projections can provide abstract category knowledge that would alter our perception (the categorical perception hypothesis) and predictive information that enables them to predict the future (the predictive coding hypothesis).