{"id":947355,"date":"2023-06-12T02:21:22","date_gmt":"2023-06-12T09:21:22","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&#038;p=947355"},"modified":"2023-09-17T23:48:20","modified_gmt":"2023-09-18T06:48:20","slug":"brain-neuroscience-workshop-2023","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/brain-neuroscience-workshop-2023\/","title":{"rendered":"Brain & Neuroscience Workshop 2023"},"content":{"rendered":"\n\n\n\n\n<p>The Microsoft Research Brain & Neuroscience Workshop aims to bring together leading experts and researchers from around the globe to explore the&nbsp;synergies between artificial intelligence (AI), the brain, and the field of neuroscience. Discussion topics include but not limit to:&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Brain-inspired artificial intelligence and machine learning methods<\/li>\n\n\n\n<li>Machine learning-based approaches for brain and neuroscience<\/li>\n\n\n\n<li>Embodied AI and neurorobotics<\/li>\n\n\n\n<li>Computational neuroscience<\/li>\n<\/ul>\n\n\n\n<p>The goal of this workshop is to share knowledge among experts in artificial intelligence, neuroscience, cognitive science and related fields. The workshop envisions and promotes new directions, articulates visions for the future, and creates strategic plans for AI-brain interdisciplinary research.<\/p>\n\n\n\n<p><strong>Organizing Committee<\/strong><\/p>\n\n\n\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/dongsli\/\">Dongsheng Li<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/dongqihan\/\">Dongqi Han<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.linkedin.com\/in\/miran-lee-37996b36\/\">Miran Lee<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n\n\n<p>Date: 9:00am \u2013 12:00pm China Standard Time, June 20, 2023<\/p>\n\n\n\n<figure class=\"wp-block-table\">\n<table>\n<thead>\n<tr>\n<th style=\"width:12%;\"><strong>Time<\/strong><\/th>\n<th style=\"width:14%;\"><strong>Session<\/strong><\/th>\n<th style=\"width:50%;\"><strong>Title<\/strong><\/th>\n<th style=\"width:24%;\"><strong>Speaker<\/strong><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>9:00-9:05<\/td>\n<td>Opening & Introduction<\/td>\n<td>Opening remarks<\/td>\n<td>Dongsheng Li, MSR Asia<\/td>\n<\/tr>\n<tr>\n<td rowspan=\"3\">9:05-10:35<\/td>\n<td rowspan=\"3\">Invited Talks<\/td>\n<td>Exploring robotic minds using the free energy principle\n<br><br>\nAbstract:<br>\nThe aim of this research is to investigate the mechanisms underlying embodied cognition, with a particular focus on the top-down and bottom-up interactions between an organism and its environment. We developed a variational RNN model based on the free energy principle and applied it to a series of robotics experiments, using predictive coding and active inference frameworks. Our results demonstrate that the estimated precision in the top-down prediction develops differently depending on a meta-parameter known as the meta-prior, w. A robot trained with a larger w tends to exhibit stronger top-down intention, while a robot trained with a smaller w exhibits weaker top-down intention and tends to follow the bottom-up sensation more closely. We discuss how the precision structure developed could affect embodied cognitive processes in interactions with physical objects and other agents in social cognition. Additionally, we speculate on the potential for online adaptation of the meta-prior and its impact on embodied cognition.\n<\/td>\n<td>Prof. Jun Tani, Okinawa Institute of Science and Technology (OIST)<\/td>\n<\/tr>\n<tr>\n\n<td>Brain design principles of efficient neural information processing\n<br><br>\nAbstract:<br>\nThe evolution of biological brains has led to the development of energy-intensive networks with diverse intelligent functions. This study aims to examine the trade-off between energy consumption and brain complexity in biological evolution, and explore how the biological brain efficiently relocates its energy for computational functions and information communications to improve artificial intelligence design. Through re-examining previous studies, we uncovered underestimations of the size of the axon, number of synapses, neurotransmitter release probability in the human brain, and differences between excitatory and inhibitory neurons. We also relocated the glial energy of Ca2+ influx and maintaining resting potential to communication. Our calculations show that the ratio of computation to communication in the human and rat brain is considerably different from previous estimates, with a range of 1:1.6 to 1:3.6 and 1:1.54 to 1:0.88, respectively. The high human brain communication is due to costly white matter, indicating increased information exchange between brain regions to perform prodigious functions. Computers have a ratio of 1:9 to 1:209 due to high energy consumption during operations and data movement. These findings suggest that the biological brain is highly energy-efficient in communication and computation, with an energy cost per bit that is lower than that of a computer. These insights may help to enlighten the development of more energy-efficient neuromorphic chips and deep learning algorithms.\n<\/td>\n<td>Prof. Yuguo Yu, Fudan University<\/td>\n<\/tr>\n<tr>\n\n<td>Brain-like AI does not think like brains\n<br><br>\nAbstract:<br>\nIt is increasingly hard to distinguish AI from humans based on their behavior. That said, human-like, brain-like AI does not necessarily think like a brain. One way to investigate this issue is to examine fundamental questions that are easy to resolve for the brain but not for AI, such as goal-directed learning, metacognition, predictive cognition, and intuitive physics.\nFirst, I will introduce a framework to build reinforcement learning (RL) models from human behavior data without underfitting and overfitting (Brain\u21a6AI), making it possible to discover neural evidence of human metacognitive reinforcement learning. Second, I will present a new framework, neural task control, in which an auxiliary RL algorithm learns to create a synthetic experience that guides human RL at the behavioral and neural level (AI\u21a6Brain). Since this control framework broadens human experience, it will guide us to develop new hypotheses about brain functions and ultimately initiate a recursive research process: ((Brain\u21a6AI)\u21a6Brain)\u21a6\u2026\n<\/td>\n<td>Prof. Sang Wan Lee, KAIST<\/td>\n<\/tr>\n\n<tr>\n<td rowspan=\"2\">10:35-11:15<\/td>\n<td rowspan=\"2\">MSR Asia Talks<\/td>\n<td>Goals and habits in synergy: A variational Bayesian framework for behavior\n<br><br>\nAbstract:<br>\nBehaving efficiently and flexibly is crucial for biological agents and embodied AI. It is well recognized that behavior is classified into two types: reward-maximizing habitual behavior, which is fast but inflexible; and goal-directed behavior, which is flexible but slow. These behaviors are typically considered managed by two distinct systems in the brain. Here, we propose a unified approach based on variational Bayesian theory, incorporating both behaviors into a single deep learning framework using a probabilistic latent variable called &#8220;intention&#8221;. Habitual behavior is generated using the goal-less, prior distribution of intention, while goal-directed behavior uses the posterior distribution of intention that conditions on the goal. This novel Bayesian framework enables skill sharing between the two behaviors, and by leveraging predictive coding, it enables seamless generalization from habitual to goal-directed behavior without requiring additional training. Our work suggests a fresh perspective for cognitive science and embodied AI, yielding greater integration between habitual and goal-directed behaviors.\n<\/td>\n<td>Dongqi Han, MSR Asia<\/td>\n<\/tr>\n<tr>\n\n<td>Rapid Context Inference in a Thalamocortical Model using Recurrent Neural Network\n<br><br>\nAbstract:<br>\nCognitive flexibility is a fundamental ability that enables humans and animals to exhibit appropriate behaviors in various contexts. The thalamocortical interactions between the prefrontal cortex (PFC) and the mediodorsal thalamus (MD) have been identified as crucial for inferring temporal context, a critical component of cognitive flexibility. However, the neural mechanism responsible for context inference remains unknown. To address this issue, we propose a PFC-MD neural circuit model that utilizes a Hebbian plasticity rule to support rapid, online context inference. Specifically, the model MD thalamus can infer temporal contexts from prefrontal inputs within a few trials. This is achieved through the use of PFC-to-MD synaptic plasticity with pre-synaptic traces and adaptive thresholding, along with winner-take-all normalization in the MD. Furthermore, our model thalamus gates context-irrelevant neurons in the PFC, thus facilitating continual learning. We evaluate our model performance by having it sequentially learn various cognitive tasks. Incorporating an MD-like component alleviates catastrophic forgetting of previously learned contexts and demonstrates the transfer of knowledge to future contexts.\n<\/td>\n<td>Weilong Zheng, MSR Asia & Shanghai Jiao Tong University (SJTU)<\/td>\n<\/tr>\n<tr>\n<td>11:15-11:55<\/td>\n<td>Panel Discussion<\/td>\n<td>Challenges and opportunities for synergies between neuroscience and AI<\/td>\n<td>Moderator: Miran Lee, MSR Asia<br><br>\nPanelists:\u00a0\n<ul style=\"padding-left:18px\">\n<li>Prof. Jun Tani, Okinawa Institute of Science and Technology (OIST)<\/li>\n<li>Prof. Yuguo Yu, Fudan University<\/li>\n<li>Prof. Sang Wan Lee, KAIST<\/li>\n<li>Dongsheng Li, MSR Asia<\/li>\n<li>Dongqi Han, MSR Asia<\/li>\n<li>Weilong Zheng, MSR Asia & Shanghai Jiao Tong University (SJTU)<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<tr>\n<td>11:55-12:00<\/td>\n<td>Closing<\/td>\n<td>Closing remarks<\/td>\n<td>Dongsheng Li, MSR Asia<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n\n\n\n<p style=\"margin-bottom:50px;\"><\/p>\n\n\n\n\n\n<div class=\"wp-block-media-text has-vertical-margin-small  has-vertical-padding-none  is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\" data-bi-an=\"media-text\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"345\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Dongqi-Han.png\" alt=\"a boy wearing glasses and smiling at the camera\" class=\"wp-image-947706 size-full\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Dongqi-Han.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Dongqi-Han-261x300.png 261w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Dongqi-Han-157x180.png 157w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/figure><div class=\"wp-block-media-text__content\" data-bi-an=\"media-text\">\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/dongqihan\/\">Dongqi Han<\/a>, MSR Asia<\/p>\n\n\n\n<p>Dongqi Han got B.S. degree in physics from university of science and technology of China and Ph.D. degree from Okinawa institute of science and technology. He joined MSRA Shanghai lab in 2022 as a researcher. Dongqi is interested in artificial and biological neural networks and the interaction between deep learning and neuroscience.<\/p>\n<\/div><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-media-text has-vertical-margin-small  has-vertical-padding-none  is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\" data-bi-an=\"media-text\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"345\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Miran_Lee.png\" alt=\"photo\" class=\"wp-image-948171 size-full\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Miran_Lee.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Miran_Lee-261x300.png 261w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Miran_Lee-157x180.png 157w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/figure><div class=\"wp-block-media-text__content\" data-bi-an=\"media-text\">\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.linkedin.com\/in\/miran-lee-37996b36\/\">Miran Lee<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, MSR Asia<\/p>\n\n\n\n<p>Miran Lee is a Director of Microsoft Research Outreach Group at Microsoft Research responsible for academic collaboration in Korea and the Asia-Pacific region. Miran joined Microsoft Research Asia in 2005 as a university relations manager to build long-term and mutually beneficial relations with academia. She is based in Korea, where she engages with leading research universities, research institutes, and relevant government agencies. She establishes strategies and directions, identifies business opportunities, designs various programs and projects, and manages the budget. She works with students, researchers, faculty members, and university administrators to build strong partnerships, and works closely with the research groups at Microsoft Research, focusing on research collaboration, curriculum development, talent fostering, and academic exchanges. She has successfully run many global and regional programs such as Gaming & Graphics, Web-Scale NLP, Machine Translation, eHealth, SORA (Software Radio), Kinect, Microsoft Azure for Research, and Contents Creation. She\u2019s currently leading 2 themes, \u2018Discovery\u2019 and \u2019Health and Life Science\u2019 as a member of global v-team.<\/p>\n\n\n\n<p>Before her current role, Miran Lee co-founded Smart Systems, which specializes in IT outsourcing services in Illinois, United States. As CEO of Smart Systems, she successfully led the business with more than 100 percent annual growth. From 1993 to 2002, she worked at British Telecom Korea in various positions ranging from systems engineer to account director to vice president. Lee also worked at Samsung SDS, where she was responsible for International VAN (Value Added Network) businesses and led the International VAN business team. She started her business career as a system developer at General Electric Information Services, where she developed email, EDI, and in-house applications. Miran Lee was an adjunct professor in the Telecommunication Department at Anyang University for two years (2001\u20132002).<\/p>\n<\/div><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-media-text has-vertical-margin-small  has-vertical-padding-none  is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\" data-bi-an=\"media-text\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"345\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Sang-Wan-Lee.jpg\" alt=\"a man wearing glasses and smiling at the camera\" class=\"wp-image-947709 size-full\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Sang-Wan-Lee.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Sang-Wan-Lee-261x300.jpg 261w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Sang-Wan-Lee-157x180.jpg 157w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/figure><div class=\"wp-block-media-text__content\" data-bi-an=\"media-text\">\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/aibrain.kaist.ac.kr\/sang-wan-lee\">Sang Wan Lee<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, KAIST<\/p>\n\n\n\n<p>Sang Wan Lee is an associate professor in the&nbsp;Department of Bio and Brain Engineering&nbsp;at KAIST, a faculty member of the&nbsp;Program of Brain and Cognitive Engineering,&nbsp;KAIST Institute for Artificial Intelligence, and&nbsp;KAIST Institute for Health, Science, and Technology. He is a founding director of the KAIST Center for Neuroscience-inspired Artificial Intelligence. He served as the head of the Program of Brain and Cognitive Engineering during 2021-2023.&#8221;<\/p>\n\n\n\n<p>Dr. Lee received his Ph.D. in Electrical Engineering and Computer Science from&nbsp;KAIST&nbsp;in 2009, working with&nbsp;Zeungnam Bien. He was a postdoctoral associate at&nbsp;MIT, working with&nbsp;Tomaso Poggio, followed by a Della Martin postdoctoral scholar at&nbsp;Caltech, working with&nbsp;John O&#8217;Doherty&nbsp;and&nbsp;Shinsuke Shimojo.<\/p>\n\n\n\n<p>He is the recipient of the&nbsp;Google Faculty Research Award&nbsp;(2016) and&nbsp;IBM Academic Awards&nbsp;(2021). I also won a few awards from KAIST, including KAIST Songam Distinguished Research Award (2019), KAIST Institute Faculty Award (2019), and KAIST International Cooperation Award (2022).<\/p>\n\n\n\n<p>His research focuses on understanding how the brain learns and makes inferences. To address this question, He has put together ideas from developing fields of machine learning and computational neuroscience. The approach is two-fold: 1) \u201cBrain\u21a6AI\u201d aimed at understanding&nbsp;how the brain learns from a machine learning standpoint, and 2) \u201cAI\u21a6Brain\u201d aimed at understanding&nbsp;why&nbsp;such neural processes occur.<\/p>\n<\/div><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-media-text has-vertical-margin-small  has-vertical-padding-none  is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\" data-bi-an=\"media-text\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"345\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Dongsheng-Li.jpg\" alt=\"photo\" class=\"wp-image-948198 size-full\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Dongsheng-Li.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Dongsheng-Li-261x300.jpg 261w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Dongsheng-Li-157x180.jpg 157w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/figure><div class=\"wp-block-media-text__content\" data-bi-an=\"media-text\">\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/dongsli\/\">Dongsheng Li<\/a>, MSR Asia<\/p>\n\n\n\n<p>Dongsheng Li is a principal research manager with Microsoft Research Asia, Shanghai, China. Meanwhile, he is an adjunct professor with School of Computer Science, Fudan University, Shanghai, China. He obtained Ph.D. from school of computer science, Fudan University in 2012, and B.E. from department of computer science and technology, University of Science and Technology of China (USTC) in 2007.<\/p>\n\n\n\n<p>His research interests focus on machine learning and its applications. For machine learning research, he is interested to recommendation algorithms, responsible AI, bio-inspired neural network, graph neural network, computer vision, sequential learning, reinforcement learning and fundamental technologies for speech and natural language processing. For machine learning applications, he is interested to applying AI for healthcare realted problems, e.g., AI-assisted medical diagnosis, prediction based on bio-medical graphs, etc.<\/p>\n<\/div><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-media-text has-vertical-margin-small  has-vertical-padding-none  is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\" data-bi-an=\"media-text\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"345\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/pic-kns-jun-tani.jpeg\" alt=\"photo\" class=\"wp-image-947712 size-full\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/pic-kns-jun-tani.jpeg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/pic-kns-jun-tani-261x300.jpeg 261w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/pic-kns-jun-tani-157x180.jpeg 157w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/figure><div class=\"wp-block-media-text__content\" data-bi-an=\"media-text\">\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/groups.oist.jp\/cnru\/jun-tani\">Jun Tani<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Okinawa Institute of Science and Technology (OIST)<\/p>\n\n\n\n<p>Jun Tani received the D.Eng. degree from Sophia University, Tokyo in 1995. He started his research career with Sony Computer Science Lab. in 1993. He became a PI in RIKEN Brain Science Institute in 2001. He became a Professor at KAIST, South Korea in 2012. He is currently a Professor at OIST. He is also a visiting professor of the Technical University of Munich. His current research interests include cognitive neuroscience, developmental psychology, phenomenology, complex adaptive systems, and robotics. He is an author of \u201cExploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena.&#8221; published from Oxford Univ. Press in 2016.<\/p>\n<\/div><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-media-text has-vertical-margin-small  has-vertical-padding-none  is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\" data-bi-an=\"media-text\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"345\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Yuguo-Yu.jpg\" alt=\"photo\" class=\"wp-image-947715 size-full\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Yuguo-Yu.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Yuguo-Yu-261x300.jpg 261w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Yuguo-Yu-157x180.jpg 157w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/figure><div class=\"wp-block-media-text__content\" data-bi-an=\"media-text\">\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/iics.fudan.edu.cn\/41\/dd\/c33358a410077\/page.htm\">Yuguo Yu<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Fudan University<\/p>\n\n\n\n<p>Yuguo Yu is a full professor at the Research Institute of Intelligent and Complex Systems, Fudan University. He received his Ph.D. in Physics from Nanjing University in 2001, and conducted postdoctoral research in Computational Neuroscience at Carnegie Mellon University (2001 \u2013 2004). He was later employed as an Associate Research Scientist at the medical school of Yale University (2005 &#8211; 2012). Dr. Yu&#8217;s research focuses on the mechanisms of neural information processing and dynamics, large-scale spiking neural network modeling, neuro-energetics, and brain-inspired intelligence. He has published over 70 peer-reviewed journal papers in prestigious publications such as Nature, PNAS, Neuron, Physical Review Letters, J Neurosci, PLoS Comp Biol, among others. He has been awarded the Professorship of Eastern Scholar at Shanghai Institutions of Higher Learning in 2013 and 2017, as well as the Shanghai Excellent Academic Leader award in 2021.<\/p>\n<\/div><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div class=\"wp-block-media-text has-vertical-margin-small  has-vertical-padding-none  is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\" data-bi-an=\"media-text\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"345\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Weilong-Zheng.png\" alt=\"photo\" class=\"wp-image-948207 size-full\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Weilong-Zheng.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Weilong-Zheng-261x300.png 261w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Weilong-Zheng-157x180.png 157w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/figure><div class=\"wp-block-media-text__content\" data-bi-an=\"media-text\">\n<p><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/bcmi.sjtu.edu.cn\/~zhengweilong\/\">Weilong Zheng<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, Shanghai Jiao Tong University (SJTU) & MSR Asia<\/p>\n\n\n\n<p>Wei-Long Zheng is a tenured-track Associate Professor in Department of Computer Science and Engineering, Shanghai Jiao Tong University, and a visiting researcher at Microsoft Research Asia. He was a postdoc associate in the Department of Brain and Cognitive Science at Massachusetts Institute of Technology (MIT), USA and a research fellow in the Department of Neurology, Massachusetts General Hospital, Harvard Medical School, USA. He received the Ph.D. degree in computer science with Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. He received ACM Multimedia Top Paper Award 2022, IEEE Transactions on Affective Computing Best Paper Award 2021, and IEEE Transactions on Autonomous Mental Development Outstanding Paper Award 2018. His research focuses on computational neuroscience, affective computing, brain-computer interfaces, and machine learning.<\/p>\n<\/div><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n","protected":false},"excerpt":{"rendered":"<p>The Microsoft Research Brain & Neuroscience Workshop aims to bring together leading experts and researchers from around the globe to explore the&nbsp;synergies between artificial intelligence (AI), the brain, and the field of neuroscience. Discussion topics include but not limit to:&nbsp;&nbsp;&nbsp; The goal of this workshop is to share knowledge among experts in artificial intelligence, neuroscience, [&hellip;]<\/p>\n","protected":false},"featured_media":947739,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"msr_startdate":"2023-06-20","msr_enddate":"2023-06-20","msr_location":"Virtual","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"","msr_hide_region":false,"msr_private_event":false,"footnotes":""},"research-area":[13556],"msr-region":[],"msr-event-type":[210063],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-947355","msr-event","type-msr-event","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-event-type-workshop","msr-locale-en_us"],"msr_about":"<!-- wp:msr\/event-details {\"title\":\"Brain \\u0026amp; Neuroscience Workshop 2023\",\"image\":{\"id\":947739,\"url\":\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/brain-neuroscience-workshop-2023-kv.jpg\",\"alt\":\"background pattern\"}} \/-->\n\n<!-- wp:msr\/content-tabs -->\n<!-- wp:msr\/content-tab -->\n<!-- wp:paragraph -->\n<p>The Microsoft Research Brain &amp; Neuroscience Workshop aims to bring together leading experts and researchers from around the globe to explore the&nbsp;synergies between artificial intelligence (AI), the brain, and the field of neuroscience. Discussion topics include but not limit to:&nbsp;&nbsp;&nbsp;<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:list -->\n<ul><!-- wp:list-item -->\n<li>Brain-inspired artificial intelligence and machine learning methods<\/li>\n<!-- \/wp:list-item -->\n\n<!-- wp:list-item -->\n<li>Machine learning-based approaches for brain and neuroscience<\/li>\n<!-- \/wp:list-item -->\n\n<!-- wp:list-item -->\n<li>Embodied AI and neurorobotics<\/li>\n<!-- \/wp:list-item -->\n\n<!-- wp:list-item -->\n<li>Computational neuroscience<\/li>\n<!-- \/wp:list-item --><\/ul>\n<!-- \/wp:list -->\n\n<!-- wp:paragraph -->\n<p>The goal of this workshop is to share knowledge among experts in artificial intelligence, neuroscience, cognitive science and related fields. The workshop envisions and promotes new directions, articulates visions for the future, and creates strategic plans for AI-brain interdisciplinary research.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong>Organizing Committee<\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/dongsli\/\">Dongsheng Li<\/a>, <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/dongqihan\/\">Dongqi Han<\/a>, <a href=\"https:\/\/www.linkedin.com\/in\/miran-lee-37996b36\/\">Miran Lee<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"40px\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<!-- \/wp:spacer -->\n<!-- \/wp:msr\/content-tab -->\n\n<!-- wp:msr\/content-tab {\"title\":\"Agenda\"} -->\n<!-- wp:paragraph -->\n<p>Date: 9:00am \u2013 12:00pm China Standard Time, June 20, 2023<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:html -->\n<figure class=\"wp-block-table\">\n<table>\n<thead>\n<tr>\n<th style=\"width:12%;\"><strong>Time<\/strong><\/th>\n<th style=\"width:14%;\"><strong>Session<\/strong><\/th>\n<th style=\"width:50%;\"><strong>Title<\/strong><\/th>\n<th style=\"width:24%;\"><strong>Speaker<\/strong><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>9:00-9:05<\/td>\n<td>Opening &amp; Introduction<\/td>\n<td>Opening remarks<\/td>\n<td>Dongsheng Li, MSR Asia<\/td>\n<\/tr>\n<tr>\n<td rowspan=\"3\">9:05-10:35<\/td>\n<td rowspan=\"3\">Invited Talks<\/td>\n<td>Exploring robotic minds using the free energy principle\n<br><br>\nAbstract:<br>\nThe aim of this research is to investigate the mechanisms underlying embodied cognition, with a particular focus on the top-down and bottom-up interactions between an organism and its environment. We developed a variational RNN model based on the free energy principle and applied it to a series of robotics experiments, using predictive coding and active inference frameworks. Our results demonstrate that the estimated precision in the top-down prediction develops differently depending on a meta-parameter known as the meta-prior, w. A robot trained with a larger w tends to exhibit stronger top-down intention, while a robot trained with a smaller w exhibits weaker top-down intention and tends to follow the bottom-up sensation more closely. We discuss how the precision structure developed could affect embodied cognitive processes in interactions with physical objects and other agents in social cognition. Additionally, we speculate on the potential for online adaptation of the meta-prior and its impact on embodied cognition.\n<\/td>\n<td>Prof. Jun Tani, Okinawa Institute of Science and Technology (OIST)<\/td>\n<\/tr>\n<tr>\n\n<td>Brain design principles of efficient neural information processing\n<br><br>\nAbstract:<br>\nThe evolution of biological brains has led to the development of energy-intensive networks with diverse intelligent functions. This study aims to examine the trade-off between energy consumption and brain complexity in biological evolution, and explore how the biological brain efficiently relocates its energy for computational functions and information communications to improve artificial intelligence design. Through re-examining previous studies, we uncovered underestimations of the size of the axon, number of synapses, neurotransmitter release probability in the human brain, and differences between excitatory and inhibitory neurons. We also relocated the glial energy of Ca2+ influx and maintaining resting potential to communication. Our calculations show that the ratio of computation to communication in the human and rat brain is considerably different from previous estimates, with a range of 1:1.6 to 1:3.6 and 1:1.54 to 1:0.88, respectively. The high human brain communication is due to costly white matter, indicating increased information exchange between brain regions to perform prodigious functions. Computers have a ratio of 1:9 to 1:209 due to high energy consumption during operations and data movement. These findings suggest that the biological brain is highly energy-efficient in communication and computation, with an energy cost per bit that is lower than that of a computer. These insights may help to enlighten the development of more energy-efficient neuromorphic chips and deep learning algorithms.\n<\/td>\n<td>Prof. Yuguo Yu, Fudan University<\/td>\n<\/tr>\n<tr>\n\n<td>Brain-like AI does not think like brains\n<br><br>\nAbstract:<br>\nIt is increasingly hard to distinguish AI from humans based on their behavior. That said, human-like, brain-like AI does not necessarily think like a brain. One way to investigate this issue is to examine fundamental questions that are easy to resolve for the brain but not for AI, such as goal-directed learning, metacognition, predictive cognition, and intuitive physics.\nFirst, I will introduce a framework to build reinforcement learning (RL) models from human behavior data without underfitting and overfitting (Brain\u21a6AI), making it possible to discover neural evidence of human metacognitive reinforcement learning. Second, I will present a new framework, neural task control, in which an auxiliary RL algorithm learns to create a synthetic experience that guides human RL at the behavioral and neural level (AI\u21a6Brain). Since this control framework broadens human experience, it will guide us to develop new hypotheses about brain functions and ultimately initiate a recursive research process: ((Brain\u21a6AI)\u21a6Brain)\u21a6\u2026\n<\/td>\n<td>Prof. Sang Wan Lee, KAIST<\/td>\n<\/tr>\n\n<tr>\n<td rowspan=\"2\">10:35-11:15<\/td>\n<td rowspan=\"2\">MSR Asia Talks<\/td>\n<td>Goals and habits in synergy: A variational Bayesian framework for behavior\n<br><br>\nAbstract:<br>\nBehaving efficiently and flexibly is crucial for biological agents and embodied AI. It is well recognized that behavior is classified into two types: reward-maximizing habitual behavior, which is fast but inflexible; and goal-directed behavior, which is flexible but slow. These behaviors are typically considered managed by two distinct systems in the brain. Here, we propose a unified approach based on variational Bayesian theory, incorporating both behaviors into a single deep learning framework using a probabilistic latent variable called \"intention\". Habitual behavior is generated using the goal-less, prior distribution of intention, while goal-directed behavior uses the posterior distribution of intention that conditions on the goal. This novel Bayesian framework enables skill sharing between the two behaviors, and by leveraging predictive coding, it enables seamless generalization from habitual to goal-directed behavior without requiring additional training. Our work suggests a fresh perspective for cognitive science and embodied AI, yielding greater integration between habitual and goal-directed behaviors.\n<\/td>\n<td>Dongqi Han, MSR Asia<\/td>\n<\/tr>\n<tr>\n\n<td>Rapid Context Inference in a Thalamocortical Model using Recurrent Neural Network\n<br><br>\nAbstract:<br>\nCognitive flexibility is a fundamental ability that enables humans and animals to exhibit appropriate behaviors in various contexts. The thalamocortical interactions between the prefrontal cortex (PFC) and the mediodorsal thalamus (MD) have been identified as crucial for inferring temporal context, a critical component of cognitive flexibility. However, the neural mechanism responsible for context inference remains unknown. To address this issue, we propose a PFC-MD neural circuit model that utilizes a Hebbian plasticity rule to support rapid, online context inference. Specifically, the model MD thalamus can infer temporal contexts from prefrontal inputs within a few trials. This is achieved through the use of PFC-to-MD synaptic plasticity with pre-synaptic traces and adaptive thresholding, along with winner-take-all normalization in the MD. Furthermore, our model thalamus gates context-irrelevant neurons in the PFC, thus facilitating continual learning. We evaluate our model performance by having it sequentially learn various cognitive tasks. Incorporating an MD-like component alleviates catastrophic forgetting of previously learned contexts and demonstrates the transfer of knowledge to future contexts.\n<\/td>\n<td>Weilong Zheng, MSR Asia &amp; Shanghai Jiao Tong University (SJTU)<\/td>\n<\/tr>\n<tr>\n<td>11:15-11:55<\/td>\n<td>Panel Discussion<\/td>\n<td>Challenges and opportunities for synergies between neuroscience and AI<\/td>\n<td>Moderator: Miran Lee, MSR Asia<br><br>\nPanelists:\u00a0\n<ul style=\"padding-left:18px\">\n<li>Prof. Jun Tani, Okinawa Institute of Science and Technology (OIST)<\/li>\n<li>Prof. Yuguo Yu, Fudan University<\/li>\n<li>Prof. Sang Wan Lee, KAIST<\/li>\n<li>Dongsheng Li, MSR Asia<\/li>\n<li>Dongqi Han, MSR Asia<\/li>\n<li>Weilong Zheng, MSR Asia &amp; Shanghai Jiao Tong University (SJTU)<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<tr>\n<td>11:55-12:00<\/td>\n<td>Closing<\/td>\n<td>Closing remarks<\/td>\n<td>Dongsheng Li, MSR Asia<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<!-- \/wp:html -->\n\n<!-- wp:html -->\n<p style=\"margin-bottom:50px;\"><\/p>\n<!-- \/wp:html -->\n<!-- \/wp:msr\/content-tab -->\n\n<!-- wp:msr\/content-tab {\"title\":\"Speakers\"} -->\n<!-- wp:media-text {\"mediaId\":947706,\"mediaLink\":\"https:\/\/www.microsoft.com\/en-us\/research\/?attachment_id=947706\",\"mediaType\":\"image\",\"mediaWidth\":15,\"backgroundColor\":\"\"} -->\n<div class=\"wp-block-media-text is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\"><figure class=\"wp-block-media-text__media\"><img src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Dongqi-Han.png\" alt=\"a boy wearing glasses and smiling at the camera\" class=\"wp-image-947706 size-full\"\/><\/figure><div class=\"wp-block-media-text__content\"><!-- wp:paragraph -->\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/dongqihan\/\">Dongqi Han<\/a>, MSR Asia<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Dongqi Han got B.S. degree in physics from university of science and technology of China and Ph.D. degree from Okinawa institute of science and technology. He joined MSRA Shanghai lab in 2022 as a researcher. Dongqi is interested in artificial and biological neural networks and the interaction between deep learning and neuroscience.<\/p>\n<!-- \/wp:paragraph --><\/div><\/div>\n<!-- \/wp:media-text -->\n\n<!-- wp:separator -->\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<!-- \/wp:separator -->\n\n<!-- wp:media-text {\"mediaId\":948171,\"mediaLink\":\"https:\/\/www.microsoft.com\/en-us\/research\/?attachment_id=948171\",\"mediaType\":\"image\",\"mediaWidth\":15,\"backgroundColor\":\"\"} -->\n<div class=\"wp-block-media-text is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\"><figure class=\"wp-block-media-text__media\"><img src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Miran_Lee.png\" alt=\"photo\" class=\"wp-image-948171 size-full\"\/><\/figure><div class=\"wp-block-media-text__content\"><!-- wp:paragraph {\"placeholder\":\"Content\u2026\"} -->\n<p><a href=\"https:\/\/www.linkedin.com\/in\/miran-lee-37996b36\/\">Miran Lee<\/a>, MSR Asia<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Miran Lee is a Director of Microsoft Research Outreach Group at Microsoft Research responsible for academic collaboration in Korea and the Asia-Pacific region. Miran joined Microsoft Research Asia in 2005 as a university relations manager to build long-term and mutually beneficial relations with academia. She is based in Korea, where she engages with leading research universities, research institutes, and relevant government agencies. She establishes strategies and directions, identifies business opportunities, designs various programs and projects, and manages the budget. She works with students, researchers, faculty members, and university administrators to build strong partnerships, and works closely with the research groups at Microsoft Research, focusing on research collaboration, curriculum development, talent fostering, and academic exchanges. She has successfully run many global and regional programs such as Gaming &amp; Graphics, Web-Scale NLP, Machine Translation, eHealth, SORA (Software Radio), Kinect, Microsoft Azure for Research, and Contents Creation. She\u2019s currently leading 2 themes, \u2018Discovery\u2019 and \u2019Health and Life Science\u2019 as a member of global v-team.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Before her current role, Miran Lee co-founded Smart Systems, which specializes in IT outsourcing services in Illinois, United States. As CEO of Smart Systems, she successfully led the business with more than 100 percent annual growth. From 1993 to 2002, she worked at British Telecom Korea in various positions ranging from systems engineer to account director to vice president. Lee also worked at Samsung SDS, where she was responsible for International VAN (Value Added Network) businesses and led the International VAN business team. She started her business career as a system developer at General Electric Information Services, where she developed email, EDI, and in-house applications. Miran Lee was an adjunct professor in the Telecommunication Department at Anyang University for two years (2001\u20132002).<\/p>\n<!-- \/wp:paragraph --><\/div><\/div>\n<!-- \/wp:media-text -->\n\n<!-- wp:separator -->\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<!-- \/wp:separator -->\n\n<!-- wp:media-text {\"mediaId\":947709,\"mediaLink\":\"https:\/\/www.microsoft.com\/en-us\/research\/?attachment_id=947709\",\"mediaType\":\"image\",\"mediaWidth\":15,\"backgroundColor\":\"\"} -->\n<div class=\"wp-block-media-text is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\"><figure class=\"wp-block-media-text__media\"><img src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Sang-Wan-Lee.jpg\" alt=\"a man wearing glasses and smiling at the camera\" class=\"wp-image-947709 size-full\"\/><\/figure><div class=\"wp-block-media-text__content\"><!-- wp:paragraph -->\n<p><a href=\"https:\/\/aibrain.kaist.ac.kr\/sang-wan-lee\">Sang Wan Lee<\/a>, KAIST<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Sang Wan Lee is an associate professor in the&nbsp;Department of Bio and Brain Engineering&nbsp;at KAIST, a faculty member of the&nbsp;Program of Brain and Cognitive Engineering,&nbsp;KAIST Institute for Artificial Intelligence, and&nbsp;KAIST Institute for Health, Science, and Technology. He is a founding director of the KAIST Center for Neuroscience-inspired Artificial Intelligence. He served as the head of the Program of Brain and Cognitive Engineering during 2021-2023.\"<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Dr. Lee received his Ph.D. in Electrical Engineering and Computer Science from&nbsp;KAIST&nbsp;in 2009, working with&nbsp;Zeungnam Bien. He was a postdoctoral associate at&nbsp;MIT, working with&nbsp;Tomaso Poggio, followed by a Della Martin postdoctoral scholar at&nbsp;Caltech, working with&nbsp;John O'Doherty&nbsp;and&nbsp;Shinsuke Shimojo.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>He is the recipient of the&nbsp;Google Faculty Research Award&nbsp;(2016) and&nbsp;IBM Academic Awards&nbsp;(2021). I also won a few awards from KAIST, including KAIST Songam Distinguished Research Award (2019), KAIST Institute Faculty Award (2019), and KAIST International Cooperation Award (2022).<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>His research focuses on understanding how the brain learns and makes inferences. To address this question, He has put together ideas from developing fields of machine learning and computational neuroscience. The approach is two-fold: 1) \u201cBrain\u21a6AI\u201d aimed at understanding&nbsp;how the brain learns from a machine learning standpoint, and 2) \u201cAI\u21a6Brain\u201d aimed at understanding&nbsp;why&nbsp;such neural processes occur.<\/p>\n<!-- \/wp:paragraph --><\/div><\/div>\n<!-- \/wp:media-text -->\n\n<!-- wp:separator -->\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<!-- \/wp:separator -->\n\n<!-- wp:media-text {\"mediaId\":948198,\"mediaLink\":\"https:\/\/www.microsoft.com\/en-us\/research\/?attachment_id=948198\",\"mediaType\":\"image\",\"mediaWidth\":15,\"backgroundColor\":\"\"} -->\n<div class=\"wp-block-media-text is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\"><figure class=\"wp-block-media-text__media\"><img src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Dongsheng-Li.jpg\" alt=\"photo\" class=\"wp-image-948198 size-full\"\/><\/figure><div class=\"wp-block-media-text__content\"><!-- wp:paragraph -->\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/dongsli\/\">Dongsheng Li<\/a>, MSR Asia<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Dongsheng Li is a principal research manager with Microsoft Research Asia, Shanghai, China. Meanwhile, he is an adjunct professor with School of Computer Science, Fudan University, Shanghai, China. He obtained Ph.D. from school of computer science, Fudan University in 2012, and B.E. from department of computer science and technology, University of Science and Technology of China (USTC) in 2007.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>His research interests focus on machine learning and its applications. For machine learning research, he is interested to recommendation algorithms, responsible AI, bio-inspired neural network, graph neural network, computer vision, sequential learning, reinforcement learning and fundamental technologies for speech and natural language processing. For machine learning applications, he is interested to applying AI for healthcare realted problems, e.g., AI-assisted medical diagnosis, prediction based on bio-medical graphs, etc.<\/p>\n<!-- \/wp:paragraph --><\/div><\/div>\n<!-- \/wp:media-text -->\n\n<!-- wp:separator -->\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<!-- \/wp:separator -->\n\n<!-- wp:media-text {\"mediaId\":947712,\"mediaLink\":\"https:\/\/www.microsoft.com\/en-us\/research\/?attachment_id=947712\",\"mediaType\":\"image\",\"mediaWidth\":15,\"backgroundColor\":\"\"} -->\n<div class=\"wp-block-media-text is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\"><figure class=\"wp-block-media-text__media\"><img src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/pic-kns-jun-tani.jpeg\" alt=\"photo\" class=\"wp-image-947712 size-full\"\/><\/figure><div class=\"wp-block-media-text__content\"><!-- wp:paragraph {\"placeholder\":\"Write content\u2026\"} -->\n<p><a href=\"https:\/\/groups.oist.jp\/cnru\/jun-tani\">Jun Tani<\/a>, Okinawa Institute of Science and Technology (OIST)<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Jun Tani received the D.Eng. degree from Sophia University, Tokyo in 1995. He started his research career with Sony Computer Science Lab. in 1993. He became a PI in RIKEN Brain Science Institute in 2001. He became a Professor at KAIST, South Korea in 2012. He is currently a Professor at OIST. He is also a visiting professor of the Technical University of Munich. His current research interests include cognitive neuroscience, developmental psychology, phenomenology, complex adaptive systems, and robotics. He is an author of \u201cExploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena.\" published from Oxford Univ. Press in 2016.<\/p>\n<!-- \/wp:paragraph --><\/div><\/div>\n<!-- \/wp:media-text -->\n\n<!-- wp:separator -->\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<!-- \/wp:separator -->\n\n<!-- wp:media-text {\"mediaId\":947715,\"mediaLink\":\"https:\/\/www.microsoft.com\/en-us\/research\/?attachment_id=947715\",\"mediaType\":\"image\",\"mediaWidth\":15,\"backgroundColor\":\"\"} -->\n<div class=\"wp-block-media-text is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\"><figure class=\"wp-block-media-text__media\"><img src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Yuguo-Yu.jpg\" alt=\"photo\" class=\"wp-image-947715 size-full\"\/><\/figure><div class=\"wp-block-media-text__content\"><!-- wp:paragraph -->\n<p><a href=\"https:\/\/iics.fudan.edu.cn\/41\/dd\/c33358a410077\/page.htm\">Yuguo Yu<\/a>, Fudan University<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Yuguo Yu is a full professor at the Research Institute of Intelligent and Complex Systems, Fudan University. He received his Ph.D. in Physics from Nanjing University in 2001, and conducted postdoctoral research in Computational Neuroscience at Carnegie Mellon University (2001 \u2013 2004). He was later employed as an Associate Research Scientist at the medical school of Yale University (2005 - 2012). Dr. Yu's research focuses on the mechanisms of neural information processing and dynamics, large-scale spiking neural network modeling, neuro-energetics, and brain-inspired intelligence. He has published over 70 peer-reviewed journal papers in prestigious publications such as Nature, PNAS, Neuron, Physical Review Letters, J Neurosci, PLoS Comp Biol, among others. He has been awarded the Professorship of Eastern Scholar at Shanghai Institutions of Higher Learning in 2013 and 2017, as well as the Shanghai Excellent Academic Leader award in 2021.<\/p>\n<!-- \/wp:paragraph --><\/div><\/div>\n<!-- \/wp:media-text -->\n\n<!-- wp:separator -->\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<!-- \/wp:separator -->\n\n<!-- wp:media-text {\"mediaId\":948207,\"mediaLink\":\"https:\/\/www.microsoft.com\/en-us\/research\/?attachment_id=948207\",\"mediaType\":\"image\",\"mediaWidth\":15,\"backgroundColor\":\"\"} -->\n<div class=\"wp-block-media-text is-stacked-on-mobile\" style=\"grid-template-columns:15% auto\"><figure class=\"wp-block-media-text__media\"><img src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/Weilong-Zheng.png\" alt=\"photo\" class=\"wp-image-948207 size-full\"\/><\/figure><div class=\"wp-block-media-text__content\"><!-- wp:paragraph {\"placeholder\":\"Content\u2026\"} -->\n<p><a href=\"https:\/\/bcmi.sjtu.edu.cn\/~zhengweilong\/\">Weilong Zheng<\/a>, Shanghai Jiao Tong University (SJTU) &amp; MSR Asia<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Wei-Long Zheng is a tenured-track Associate Professor in Department of Computer Science and Engineering, Shanghai Jiao Tong University, and a visiting researcher at Microsoft Research Asia. He was a postdoc associate in the Department of Brain and Cognitive Science at Massachusetts Institute of Technology (MIT), USA and a research fellow in the Department of Neurology, Massachusetts General Hospital, Harvard Medical School, USA. He received the Ph.D. degree in computer science with Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. He received ACM Multimedia Top Paper Award 2022, IEEE Transactions on Affective Computing Best Paper Award 2021, and IEEE Transactions on Autonomous Mental Development Outstanding Paper Award 2018. His research focuses on computational neuroscience, affective computing, brain-computer interfaces, and machine learning.<\/p>\n<!-- \/wp:paragraph --><\/div><\/div>\n<!-- \/wp:media-text -->\n\n<!-- wp:separator -->\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<!-- \/wp:separator -->\n<!-- \/wp:msr\/content-tab -->\n<!-- \/wp:msr\/content-tabs -->","tab-content":[],"msr_startdate":"2023-06-20","msr_enddate":"2023-06-20","msr_event_time":"","msr_location":"Virtual","msr_event_link":"","msr_event_recording_link":"","msr_startdate_formatted":"June 20, 2023","msr_register_text":"Watch now","msr_cta_link":"","msr_cta_text":"","msr_cta_bi_name":"","featured_image_thumbnail":"<img width=\"960\" height=\"540\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/brain-neuroscience-workshop-2023-kv-960x540.jpg\" class=\"img-object-cover\" alt=\"background pattern\" decoding=\"async\" loading=\"lazy\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/brain-neuroscience-workshop-2023-kv-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/brain-neuroscience-workshop-2023-kv-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/brain-neuroscience-workshop-2023-kv-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/brain-neuroscience-workshop-2023-kv-343x193.jpg 343w, https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/06\/brain-neuroscience-workshop-2023-kv-640x360.jpg 640w\" sizes=\"(max-width: 960px) 100vw, 960px\" \/>","event_excerpt":"The Microsoft Research Brain &amp; Neuroscience Workshop aims to bring together leading experts and researchers from around the globe to explore the&nbsp;synergies between artificial intelligence (AI), the brain, and the field of neuroscience. 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