Time | 5:00-6:30 pm, Nov. 19, 2018

Venue | 清华大学医学科学楼C201

Topic 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.

Brief  Biography

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 Science，a 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. 

汪小京教授现任纽约大学杰出全球神经科学教授、斯沃茨理论神经科学中心联合主任、物理与数学兼职教授。 2012年至2017年间，汪教授担任上海纽约大学创始教务长、科研副校长，此前任耶鲁大学神经生物学教授、耶鲁大学斯沃茨理论神经科学中心主任。 汪教授是理论与计算神经科学专家，研究重点是认知功能的脑机制，尤其以在短期记忆的细胞基础、决策的神经机制、大脑细胞网内信息交流与同步、抑制神经元功能等领域的研究而著称。他的团队开创了被称为“大脑CEO”的前额皮层神经网络模型研究的前沿。近年来，汪教授的团队建立了大型脑神经网络的神经生物学仿真模型，深入研究由认知功能及控制的灵活性行为，及其在人工智能和精神医学的应用。 汪教授是美国科学促进会会士，阿尔弗雷德·斯隆研究学者奖、美国国家科学基金会CAREER奖、美国神经科学学会Swartz理论与计算神经科学奖、Goldman-Rakic认知神经科学杰出成就奖获得者，并入选中国国家“千人计划”。

References:

• 汪小京 (2009) “理论神经科学导论”《神经科学》 (第三版)，韩济生主编), 北京大学出版社, 第53章，1004-1019页.
  

• 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日. http://news.sciencenet.cn/sbhtmlnews/2010/8/235983.html?id=235983

• 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，2015年8月15日. http://news.sciencenet.cn/htmlnews/2015/8/324932.shtm

• 汪小京 (2015) “脑科学需要自己的牛顿“ ，科学网，2015年11月24日.

• http://news.sciencenet.cn/htmlnews/2015/11/332429.shtm.

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

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

Topic 2

Exploring the functional relevance of feedback projections in our brain

Bin Min

Junior Researcher

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.  

Short Biography

I received my Ph.D. in computational mathematics from Peking University in 2013 when I switched to the field of computational neuroscience. In 2013-2018, I performed my 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, I joined the new brain institute in Shanghai --- Shanghai Research Center for Brain Science and Brain-Inspired Intelligence. My 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 is able to alter our sensory perception (the categorical perception hypothesis) and predictive information that enables us to make prediction about the future (the predictive coding hypothesis). 

Invited by: 

BO HONG, Ph.D.

Department of Biomedical Engineering, School of Medicine, Tsinghua University

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