CNS 2024 | 专题研讨会：计算神经科学，脑启发智能和脑机接口

文摘 2024-09-19 09:00 福建

中国神经科学学会第十七届全国学术会议将于2024年9月26日－29日在苏州市召开，作为我国神经科学领域规模最大的学术会议，其学术质量在国内屈指可数。2024年，中国神经科学学会积极组织召集专题研讨会，通过多轮投票筛选确定51个专题研讨会。

学会将陆续推出2024年专题研讨会的详细介绍，敬请关注。

以下专题排名不分先后。

参会注册：

The 17th Annual Meeting of Chinese Neuroscience Society (cns.org.cn)

Computational Neuroscience, Brain-Inspired Intelligence and Brain Machine Interfaces

Brain Inspired Computing

Organizer:Luping Shi, Shuai Zhong

Introduction: Brain inspired computing is a new computing technology with non von Neumann architectures that draws on the basic principles of information processing in the brain and is developed for general brain inspired intelligence. Brain inspired computing systems are the cornerstone of brain inspired general intelligence and have extremely broad application prospects. Its importance is as pointed out by the EU's flagship human brain program: "Whoever wants to dominate the world economy in the next 10 to 20 years must lead in this field.". Currently, the European Union, the United States, and other countries have invested heavily in long-term support for this research, which is also one of the research focuses of the China Brain Science Program. However, this research is still in its early stages and has not yet formed a common accepted technical solution. This symposium will comprehensively discuss the research background, latest progress, strategic goals, challenges, and possible technological paths for the development of brain inspired computing from the perspectives of brain inspired computing theory, chips, software, computers, brain inspired computing cloud, and applications, in order to promote the development of brain inspired computing.

报告人

Yonghong Tian

Peking University

Dr. Yonghong Tian is currently dean of school of electronic and computer engineering，a Boya Distinguished Professor with the Department of Computer Science and Technology, Peking University, China, and is also the deputy director of Artificial Intelligence Research Center, PengCheng Laboratory, Shenzhen, China. His research interests include neuromorphic vision, brain-inspired computation and multimedia big data. He has co-authored over 200 technical articles in refereed journals such as IEEE TPAMI/TNNLS/TIP/TMM/TCSVT/TKDE/ TPDS/TCYB, ACM CSUR/TOIS/TOMM and conferences such as NeurIPS/CVPR/ICCV/AAAI/ACMMM/WWW. Meanwhile, he has owned more than 85 US and China Invention Patents, and won the Award for National Excellent Patent of China in 2016. He was the recipient of the Chinese National Science Foundation for Distinguished Young Scholars in 2018, two National Science and Technology Awards and three ministerial-level awards in China, and obtained the 2015 EURASIP Best Paper Award for Journal on Image and Video Processing, and the best paper award of IEEE BigMM 2018. He is also the main technical contributor of IEEE Standard 1857.6-2018 and 1857a-2014, and the Co-Editor of ISO/IEC DIS 39794-16.

Feng Chen

Tsinghua University

Professor Chen Feng is a tenured professor in the Department of Automation at Tsinghua University. He obtained his Bachelor's, Master's degrees in the field of Automatic Control from the Saint Petersburg State Technical University in Russia in 1994, 1996, and Ph.D. degree in Tsinghua University in 2000. In 2010, he was selected for the Ministry of Education's "New Century Excellent Talents Support Program" and served as the Deputy Director of the Beijing Key Laboratory of "Big Data Processing and Application for Security and Defense." He is also the Secretary-General of the Education Working Committee of the Automation Association.

His research interests include neuromorphic computing, artificial intelligence, probabilistic graphical models, computer vision, video analysis. He has published papers in esteemed SCI-indexed journals such as Nature, IEEE Transactions on Pattern Analysis and Machine Intelligence, Image Processing, Circuits and Systems for Video Technology, Neural Networks and Learning Systems, and Optical Letters. He has been a contributor to conferences such as ICML, NIPS, ICLR and AAAI. He has received second-class National Science and Technology Invention Award (2008), second-class Beijing Science and Technology Award (2013), and first-class Ministry of Education Technology Invention Award (2012) as the principal investigator. Currently, he is undertaking tasks related to the China Brain Project.

Shukai Duan

College of Artificial Intelligence, Southwest University

Shukai Duan is the dean and professor of the College of Artificial Intelligence, Southwest University, Chongqing, China. He is also a leading talent in scientific and technological innovation under the National High-level Talent Program, a leading talent in scientific and technological innovation under the Ministry of Science and Technology, a new century excellent talent of the Ministry of Education, and a chief scientist of the National Key Research and Development Program. He serves as the director of National Local Joint Engineering Research Center of Intelligent Transmission and Control Technology, the deputy director of the Artificial Intelligence and Robot Education Committee of the Chinese Association of Automation, and the vice chairman of the China Integrated Circuit Industry Technology Innovation Alliance. His research focuses on intelligent algorithms, brain-inspired chips, and industrial intelligent robots, etc. He has taken charge of one National Key Research and Development Program, and five National Natural Science Foundation of China (one key project and four general projects). He has published more than 300 papers in Nature Communications, IEEE TNNLS, IEEE TVLSI, IEEE TCAS-I, IEEE TCAS-II, IEEE TSMC, etc., and has been granted more than 20 national invention patents. He also serves as the associate editor of international journals, such as IEEE TNNLS, Neurocomputing, Artificial Intelligence Review, and founded the academic journal, i.e., Artificial Intelligence Science and Engineering. He was awarded the IEEE TNNLS Outstanding AE, and was selected for the ‘2021 Top 100,000 Scientists in the World’ list, as well as the ‘Top 2% Scientists in the World’ list by Stanford University in 2022 and 2023.

CHUA Yansong

China Nanhu Academy of Electronics and Information Technology

Dr. Chua graduated from the Department of Biology at the University of Freiburg in Germany, with a main research focus on computational neuroscience.

During the period of 2016 to 2019, he worked at the Singapore Institute of Science and Technology (ASTAR) and was the Principal Investigator for the project on algorithm research for the ASTAR neuromorphic program, along with five other Principal Investigators (including professors from other ASTAR research institutes, the National University of Singapore and Nanyang Technological University of Singapore). He also twice led his team to participate in the International Brain Computing Competition hosted by Tsinghua University, winning the first prize and second prize respectively.

During the period of 2019 to 2021, he led the algorithm team at the Neuromorphic Computing Group at Huawei 2012 laboratory, with members across both China and Europe. During his tenure, he won numerous innovation awards in Huawei, and applied for several patents.

Currently, Dr. Chua serves as the Director of the Neuromorphic Computing Laboratory at China Nanhu Academy of Electronics and Information Technology. His work includes the Nanhu Neuromorphic Cloud Platform (supporting up to 1 billion neurons) and Neuromorphic Computing Software. He also participated in the "Heterogeneous Neuromorphic Computing Research Platform" of the National Science and Technology Innovation 2030 "Neuroscience and Neuromorphic Computing" project, and is responsible for Project 2 (neuromorphic software platform) and participates in Project 6 (neuromorphic cloud platform).

Dr. Chua's research focuses mainly on brain inspired artificial intelligence and the co-research of brain-inspired algorithms, software and hardware. He has over 30 publications in neuroscience and neuromorphic research, and also serves as associate editors for Frontiers in Neuroscience and Neurocomputing.

Xiaoping Wang

Huazhong University of Science and Technology

Wang Xiaoping is a professor and doctoral supervisor of the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, and a Senior member of IEEE. From 1993 to 2000, she studied for bachelor's degree and master's degree in Automation School of Chongqing University, and from 2000 to 2003, she studied for Doctor's degree in Systems Engineering of Huazhong University of Science and Technology. She joined Huazhong University of Science and Technology in 2000. Her main research directions are brain-like intelligence, emotional computing, memristor circuit design and its application, etc. She has presided over one key project of National Natural Science Foundation, two surface projects, youth projects, and sub-projects of National key research and development plan, etc. She has published more than 60 papers, including 35 papers published in IEEE Transactions, and 8 authorized invention patents. She also has been the program chair of ICNC2021, ICNC2023, and organization chair of ICACI2022, etc.

Yuxiang Huan

Guangdong Institute of Intelligence Science and Technology

Yuxiang Huan is the Principal Investigator of the Lab of Brain-inspired Architectures and Super-large-scale Computing Systems at Guangdong Institute of Intelligence Science and Technology (GDIIST). Dr. Huan also serves as the director of the Research Center of Brain-inspired Computing Systems at GDIIST. Dr. Huan’s research focuses on Domain-Specific Architectures (DSA), energy-efficient accelerator designs, and large-scale brain-inspired computing systems based on DSAs. Dr. Huan is leading the design of a neuromorphic supercomputer towards the whole brain simulation, and his team has built a prototype system that is capable of simulating 1 billion neurons. Dr. Huan has published more than 40 papers in IEEE TCAS-I/II, TBioCAS, and other journals/conferences, and he has over 15 patents.

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Computational Neuroscience: Modeling Neural Dynamics and Cognitive Function

Organizer: Songting Li, Yuanyuan Mi

Introduction: The symposium "Computational Neuroscience: Modeling Neural Dynamics and Cognitive Function" delves into the realms of computational neuroscience to unravel the mysteries of neural dynamics and cognitive processes. Through advanced mathematical models, analyses and simulations, the symposium aims to showcase cutting-edge research that bridges the gap between neural mechanisms and cognitive functions. The talks in the symposium will focus on how computational frameworks can elucidate the complex interplay between synapses, neurons, and circuits, shedding light on fundamental cognitive processes such as learning, memory and decision making. Our diverse panel of experts will present their pioneering work in neural network modeling, biophysical simulations, and machine learning approaches, elucidating how these methodologies offer insights into brain function. By fostering interdisciplinary discussions, the symposium attempts to forge new paths in understanding the computational principles underlying brain dynamics and how they manifest in cognitive phenomena.

报告人

Si Wu

School of Psychology and Cognitive Science, Peking University

Si Wu's research focuses on building attractor network models to elucidate brain functions and developing a general programming platform for brain dynamics simulation.

1. Lin X, Li L, Shi B, et al. Slow and Weak Attractor Computation Embedded in Fast and Strong EI Balanced Neural Dynamics[C]. NeurIPS. 2023.

2. Chu T, Ji Z, Zuo J, et al. Oscillatory Tracking of Continuous Attractor Neural Networks Account for Phase Precession and Procession of Hippocampal Place Cells[J]. NeurIPS, 2022, 35: 33159-33172.

3.Wang C, Chen X, Zhang T, et al. BrainPy: a flexible, integrative, efficient, and extensible framework towards general-purpose brain dynamics programming[J]. Elife (accepted)

Changsong Zhou

Hong Kong Baptist University

Dr. Zhou’s research interest is dynamical processes on complex systems. His current emphasis is on analysis and modeling connectivity and activity in neural systems in collaboration with experimental neuroscientists, using the approaches of oscillatory dynamics networks and covering broad scales from network of excitatory-inhibitory neurons to interacting functional brain regions and functional EEG and cognitive variability and disorders.

1. Liang et al. Nature Communications, 2023, 14(1): 1434.

2. Liang et al. National Science Review, 2022, 9(3): nwab102.

3.Wang et al. PNAS, 2021, 118(23): e2022288118.

Yunliang Zang

Tianjin University

Yunliang Zang has focused on exploring the importance of neuronal properties on circuit functions. He has developed interesting theoretical insights into neuronal coding, learning, and robustness, with his representative work published in prestigious journals such as PNAS, Current Biology, and eLife.

1. Zang et al (2023). "Sodium channel slow inactivation normalizes firing in axons with uneven conductance distributions." Curr Biol

2. Zang, Y. and E. Marder (2023). "Neuronal morphology enhances robustness to perturbations of channel densities." PNAS

3.Zang, Y., S. Hong and E. De Schutter (2020). "Firing rate-dependent phase responses of Purkinje cells support transient oscillations." Elife .

Guozhang Chen

Peking University

Understanding the computational principles of the brain is one of the ultimate goals of human civilization, and is also key for enhancing brain-inspired computing. Guozhang Chen is committed to modeling and analyzing the function of the visual cortex. He achieved the first large-scale and ultracomplex functional model of primary visual cortex (certified by Innovation Origins and the EU Human Brain Project), and he has been revealing the organization of cortical computation.

1. Chen et al, "Data-based large-scale models provide a window into the organization of cortical computations." bioRxiv (2023): 2023-04.

2. Chen et al, Science Advances 8.44 (2022): eabq7592.

3.Chen et al, Science Advances 8.16 (2022): eabl4995.

Yuxiu Shao

Beijing Normal University

Yuxiu’s work is dedicated to unraveling the complexities of network connectivity and neuronal computations within both biological and artificial neural networks. Her goal is to bridge these domains, providing insights into their structures and dynamics, and to explore neural computational mechanisms that underlie flexible behavior, thereby enhancing our understanding of the brain and inspiring advanced neural network systems.

1. Shao Y, Ostojic S. Relating local connectivity and global dynamics in recurrent excitatory-inhibitory networks. PLOS Computational Biology. 2023 Jan 23;19(1):e1010855.

2.Molano-Mazón M, Shao Y, Duque D, Yang GR, Ostojic S, de la Rocha J. Recurrent networks endowed with structural priors explain suboptimal animal behavior. Current Biology. 2023 Feb 27;33(4):622-38.

Qianli Yang

Center for Excellence in Brain Science and Intelligence Technology, CAS

Qianli Yang’s main research direction involves using macaques and humans as experimental models to design complex behavioral paradigms, revealing computational models for high-level cognitive functions such as efficient perception and flexible decision-making. His recent work includes elucidating the behavioral structure of the monkey's strategy in Pac-Man decision-making tasks, deciphering the principles of neural codes in multisensory integration, and constructing novel theories of nonlinear choice correlation tests as statistical tools for understanding the brain's nonlinear coding principles.

1. Yang et al. (2022), ELife, 11.

2. Yang et al. (2021), Nature Communications, 12(1), 6557.

3.Zhao et al. (2023). IScience, 26(6), 106973.

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Machine Learning and Artificial Intelligence for Biological Neuroscience

Organizer: Guoqiang Yu, Lei Qu

Introduction: The past decades have witnessed the impressive advances in neurotechnology, such as large-scale high-resolution microscopy and sensitive protein sensors. However, the sophisticated algorithms leveraging the data lags behind and are in great demand to push neuroscience further. The current situation has an understandable root. The progress in this interdisciplinary field requires a close collaboration between experimental neuroscience and computer science and the master of knowledge in both fields. Therefore, the goals of this symposium are, (1) to provide a platform for both experimental neuroscientists and computer scientists to communicate and interact with each other, (2) to call for the attention to this interdisciplinary field and the joining of young scientists, and (3) to showcase successful stories in applying and developing machine learning methods for neuroscience. Due to the limit of time and potential coverage of human neuroscience data analysis in other symposiums, we will focus on the data analysis and modeling on biological neuroscience problems. The topics will include but not be limited to microscopic brain activity quantification, multi-modality multi-model brain alignment, whole brain connectome with EM data, micro reconstruction of zebrafish brain, and video-based animal behavior quantification.

报告人

Guoqiang Yu

Tsinghua University

于国强，清华大学自动化系教授，研究方向为机器学习，生物医学影像信息学和脑理论模型及其在脑科学发现和脑疾病治疗中的应用。回归清华前，任美国弗吉尼亚理工大学（Virginia Tech）电子与计算机工程系教授。曾获得美国自然科学基金会CAREER奖，Neuron和BIBM等多家期刊会议最佳论文奖。是美国NIH脑计划联盟和数据科学联盟核心成员，曾担任美国NIH和NSF资助的R01和U19系列多项科研项目首席科学家。在BMC Bioinformatics和Bioinformatics Advances等国际期刊担任副编辑，是中国生物信息学学会（筹）生物医学影像信息学专委会主任。在国际著名期刊Nature Neuroscience，Nature Medicine，Neuron, IEEE PAMI以及国际顶级学术会议NeurIPS，ICML等发表论文近百篇。

Lei Qu

Anhui University

屈磊，安徽大学电子信息工程学院教授，东南大学兼职教授，安徽大学研究生院常务副院长2020年度“宝钢优秀教师奖”获得者，国家级一流课程负责人。研究领域为机器学习、计算机视觉和生物图像信息学，在《Nature》《Nature Methods》《IEEE Transactions on Signal Processing Magazine》《IEEE Transactions on Image processing》《Bioinformatics》等期刊发表学术论文四十余篇，获专利28项。与东南大学合作开发的“全脑单细胞神经元及信息学大数据平台”获2021年度中国生命科学十大进展，“小鼠跨模态全脑一致性配准研究”获2022年度江苏省行业领域十大科技进展。获安徽省第七届自然科学优秀论文三等奖1项，安徽省科技进步三等奖1项，安徽省教学成果奖4项。

Anan Li

Huazhong University of Science and Technology

李安安，华中科技大学教授。一直致力于生物医学工程相关交叉科学领域的研究，特别是在单细胞水平重建全脑神经元网络的研究方向，具体研究兴趣包括神经光学图像大数据的处理、脑网络数据的分析和可视化、神经科学应用等。2005年和2011年，分别在华中科技大学生物医学工程专业获学士学位和博士学位，随后留校任教至今。已在Science、Nat Methods、Neuron、IEEE TMI、Adv Sci等期刊以第一或通讯作者身份发表论文30余篇。曾获得过2014年国家技术发明奖二等奖（3/8）、2013年全国百篇优秀博士学位论文奖，黄家驷生物医学工程奖技术发明一等奖（3/8）。目前是中国图象图形学学会脑图谱专业委员会委员、中国光学学会生物医学光子学专业委员会青年委员。

Xufei Du

Center for Excellence in Brain Science and Intelligence Technology, CAS

杜旭飞，2013年于中国科学院神经所获理学博士学位。博士期间开展了发育期突触发生节律性和静息态小胶质细胞生理功能两方面的研究。现任中国科学院脑科学与智能技术卓越创新中心/中国科学院神经所，全脑介观神经联接图谱绘制平台斑马鱼组主任，副研究员。主持2014年度上海市青年科技英才扬帆计划项目，2019年度入选中国科学院青年创新促进会。主要运用分子生物学、长时程活体成像、神经重构、三维图像配准、重组病毒环路示踪等方法，开展斑马鱼全脑介观神经联接图谱绘制和神经联接发育研究。工作发表在Nature Communications, Developmental Cell, Neuron等国际著名期刊上。

Qian Zheng

Zhejiang University

Qian Zheng is a tenure track professor at the College of Computer Science and Technology, Zhejiang University, China. From 2018 to 2022, he was a research fellow with the ROSE lab at Nanyang Technological University, Singapore. He has co-authored more than 40 papers. He serves as an associate editor of Neurocomputing and the area chair of PRCV 2023. He was a reviewer of many top journals and conferences in Machine Learning and Computer Vision, such as T-PAMI, IJCV, CVPR, ICCV, SIGGRAPH, NeurIPS, ICML, and ICLR. He was a guest editor of Frontiers in Neuroscience Neuroprosthetics.

Funing Li

Center for Excellence in Brain Science and Intelligence Technology, CAS

李福宁，中国科学院脑科学与智能技术卓越创新中心神经生物学博士，研究方向为以脊椎动物斑马鱼为模型，绘制具有突触分辨率与神经元类型多重标记的脊椎动物脑微观联接图谱，利用深度学习方法全面普查斑马鱼全脑细胞与突触结构分型，解析脊椎动物全脑特别是单胺类神经调质系统联接规律，构建脑启发式人工智能网络。此外，亦以蓝斑去甲肾上腺素能系统、间脑多巴胺能系统为模型，研究神经调质系统在感觉运动转换、行为抉择、全脑网络调节等方面的作用机制。参与工作发表于国际著名期刊如Nature Communications，iScience，Cell等。

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NeuroClinical Insights: Unraveling Neural Mysteries through Brain-Machine Interfaces in Clinical Neuroscience

Organizer: Yiru Fang, Rodrigo Quian Quiroga

Introduction: The symposium will delve into the cutting-edge realm of brain-machine interfaces (BMI), encompassing both invasive and non-invasive technologies facilitating direct communication between the brain and external devices. The focus on invasive BMI will highlight its unique capability to decode neural signals, offering insights into brain function and presenting solutions for neurological and psychiatric disorders. This session will feature presentations on invasive BMI advancements, showcasing high-resolution spatiotemporal data collection directly from neurons. Experts in invasive and non-invasive BMI, neuroscience, and artificial intelligence will deliver talks covering topics such as ultraflexible electrodes, integrated BMI design, memory mechanisms revealed through single-neuron recordings, and neural networks inspired by neuroscience discoveries. Attendees, including researchers and students from diverse fields like Neuroscience, Computer Science, and Engineering, are encouraged to participate in interdisciplinary discussions. The symposium aims to foster collaborations and present novel findings in computational neuroscience, contributing to the development of both invasive and non-invasive brain-inspired intelligence. By exploring these technologies, the session seeks to deepen our understanding of the brain and advance human awareness, addressing complex neurological challenges through innovative solutions.

报告人

Rodrigo Quian Quiroga

Institut Hospital del Mar

Rodrigo Quian Quiroga, Director of the Centre for Systems Neuroscience at the University of Leicester, is known for developing signal-processing methods for the analysis of electrophysiological data. He proposed a method to automatically identify the activity of neurons from extracellular recordings. The use of this technique led him to discover a new type of neuron in the human brain, the so-called “Concept Cells” - neurons that fire selectively to specific concepts. He has shown how these neurons are involved in the formation and storage of memories, particularly in the hippocampus that is known to be involved in Alzheimer’s disease, underscoring the potential clinical impact of his work.

Rodrigo Quian Quiroga will report the single-neuron level findings about emotion and visual perception based on the use of intracranial electroencephalography tool. Macro-micro electrodes are implanted in the amygdala and midfusiform gyrus of refractory epilepsy patients.

Xue Li

Center for Excellence in Brain Science and Intelligence Technology, CAS

We introduce a clinical intraoperative electrophysiological recording technique using ultraflexible microelectrodes for large-scale single-neuron recordings in the human cortex. Demonstrated in five patients, the method yields stable signals with an average signal-to-noise ratio of 4.56 and minimal cellular damage. The flexibility of the microelectrode array, resembling tissue properties, ensures reliable recordings (yielding 0.42 per neural site) and stable maintenance of action potential signals. This approach enables the study of human-specific cognitive processes, revealing coordinated neural activity during motor planning, language processing, and cognitive sequences. The ultraflexible microelectrode array emerges as a promising tool for investigating clinical dysfunctions.

Chenyu Pang

Ruijin Hospital

Empathy for others’ emotional states provides a psychological basis of pro-social behaviors in both humans and animals and help their survival This study aims to investigate the specific encoding mechanism of pain empathy in the empathic neural network (i.e. anterior insula, anterior cingulate cortex) through the use of intracranial electroencephalographic (iEEG). Epilepsy patients are presented with various painful video stimuli applied to different body parts while their brain activities are recorded. The goal is to understand how the brain encodes empathy for pain, specifically examining the neural responses associated with the perception and processing of different types of pain.

Marcus Kaiser

University of Nottingham

I will discuss the transformative potential of ultrasound technology in influencing and reshaping neural circuits. Ultrasound, traditionally known for its diagnostic applications, is now emerging as a non-invasive and precise tool for neuromodulation, offering unprecedented opportunities to modulate brain activity.

Chencheng Zhang

Ruijin Hospital Shanghai Jiaotong University School of Medicine

张陈诚，上海交通大学医学院附属瑞金医院神经外科，功能神经外科中心医师，上海脑科学与类脑研究中心“求索杰出青年”计划青年研究员。主要研究方向为脑机接口与神经调控，发表35篇SCI（第一或通讯，含共同），如National Science Review, Biological Psychiatry等，累积影响因子约155分。

获得国际帕金森病及运动障碍大会(MDS)2020年轻奖（每年2人，中国首人）；英国神经精神协会2020年Alwyn Lishman Prize；美国功能神经外科协会2020年大会最佳摘要; 2020年长三角神经科学青年科学家奖。

在学术服务方面，在Frontiers in Cell and Developmental Biology (2019 IF 5.1)担任组稿编辑；Parkinsonism and Related Disorders等主流杂志审稿人；任瑞金医院住院医师规范化培训自主委员会主席，设计开展 “临床研究能力训练课程”。

Zhijun Zhang

Affiliated Zhongda Hospital of Southeast University/Shenzhen University of Technology

张志珺，主任医师，教授，博士生导师，东南大学首席教授。东南大学附属中大医院神经内科主任，东南大学神经精神医学研究所所长；深圳理工大学（筹）生命健康学院杰出教授；兼任中国神经科学学会常务理事等。张志珺教授以临床难题为导向开展科学研究，现主要研究方向为“抑郁障碍诊疗分子生物标记物及其功能验证与靶向干预”，主要基于高同质患者，采用多组学结合生物信息学和机器学习并严格多重验证以筛选中枢源性候选分子，进而结合利用离体细胞、模式动物、类脑器官等以及神经科学技术深入揭示其功能意义和因果机制，并探讨磁神经调控和靶向干预，为抑郁障碍诊疗提供跨物种印证的实证，以推动转化。

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Progress in AI-inspired Neural Decoding Across Species and Translation into Application

Organizer: Qiang Luo, Benjamin Becker

Introduction: Rapid progress in Artificial Intelligence (AI) has led to the development of comprehensive and precise models of brain function across species, complex brain-computer interfaces, and accurate behavioral and pathological characterization at the individual level. However, these advances have predominantly remained confined to specific applications and fields. This calls for a timely reflection on the progress made in various disciplines of neural and brain-inspired sciences, and an exploration of interdisciplinary opportunities and challenges. In light of this, the international symposium will bring together experts from diverse backgrounds to showcase recent advancements in AI-inspired decoding. The symposium will foster meaningful debates on creating a synergistic framework to guide the future of AI-inspired decoding and its applications.

报告人

B.T. Thomas Yeo

National University of Singapore

I will discuss studies from my lab on mapping brain networks in thousands of individuals and how brain network features can be used to predict individual-level behavioral traits. Finally, I will end by discussing our ongoing work to individual-specific brain networks to guide the transcranial magnetic stimulation of treatment resistant depression in individuals.

Benjamin Becker

The University of Hong Kong

Benjamin Becker（德），Becker情感与动机实验室负责人和香港大学认知与情感神经科学实验室负责人（团队介绍：http://www.beckerlab.org/），香港大学认知与情感神经科学系终身教职教授，电子科技大学生命学院协议教授。目前致力于研究情绪体验的神经机制和动态行为特征，以及相应认知策略的调控，方法是利用神经影像学和基于机器学习的神经解码和调控，以及其他人类认知和情感神经科学工具（如实时神经反馈和新型药理神经调节剂），通过与靶向神经调节剂相结合，开发针对成瘾和焦虑等精神疾病的新型非侵入性治疗方案。相应的研究成果已发表在 230 多篇科学论文中，包括发表在Nature Human Behavior, Nature Communications, Advanced Science, Trends in Cognitive Sciences, PNAS, Molecular Psychiatry, American Journal of Psychiatry, and Biological Psychiatry等刊物上（> 8000 次引用，h 指数 50）。他目前担任Psychotherapy and Psychosomatics, Psychopharmacology, and Psychoradiology等国际期刊的编委，是Frontiers in Social and Affective Neuroimaging的创刊主编，也是全球脑联盟 COVID 引发的脑功能障碍倡议的共同负责人, 目前负责协调香港大学的认知科学课程。

Shaozheng Qin

Beijing Normal University

秦绍正，北京师范大学心理学部认知神经科学与学习国家重点实验室教授和IDG麦戈文脑研究院教授、PI、博士生导师，情绪与认知神经科学课题组负责人，国家中组部青年千人计划和优秀青年基金获得者；担任中国心理学会情绪与健康心理学专委会副主任、生理心理学专委会委员、中国神经科学学会应激神经生物学分会委员，并任Psychoneuroendocrinology (IF=5.52)等国际期刊编委，常年为Nature Communications、Biological Psychiatry、Journal of Neuroscience 和 Cerebral Cortex 等权威期刊审稿人。秦绍正课题组致力于解密人脑情绪与认知相互作用机制以及情绪对儿童青少年脑智发育调节作用的神经生物基础。研究成果发表于 Nature Neuroscience、Nature Communications 和 Biological Psychiatry 等权威期刊，获 Nature News 和 Nature Reviews Neuroscience 亮点论文推荐，被英国《卫报》和美国《福布斯》等国际媒体报道。

Zhen Liang

Shenzhen University

梁臻，深圳大学助理教授，香港理工大学电子及资讯工程学系博士。她于2012年至2017年期间，先后在香港神念科技有限公司和香港教育大学脑神经科学与教育研究中心任高级研究员职位。2017年至2019年在日本京都大学信息学研究院任特聘助理教授。2019年4月加入深圳大学医学部生物医学工程学院，获评深圳海外高层次人才。主要研究兴趣包括情感计算，神经工程，脑机接口，脑信号编解码建模，类脑计算，人工智能，模式识别等。已发表高水平国际学术论文50余篇，包括Cerebral Cortex、Neural Networks、IEEE Transactions on Neural Systems and Rehabilitation Engineering、Expert Systems with Applications、IEEE Transactions on Cybernetics、Pattern Recognition等Top期刊。担任客座编辑在Frontiers系列杂志和神经科学老牌期刊Journal of Neuroscience Methods组织情感计算特刊和脑机接口特刊，并于2018年IEEE System，Man and Cybernetics Society举办的BMI Hackathon中荣获IEEE SMC Winner奖。主持国家级项目一项，省市级项目两项。

Yi Luo

East China Normal University

罗艺，华东师范大学心理与认知科学学院研究员，紫江青年学者，博士生导师。主要研究兴趣是社会认知与社会决策，尤其是公平、信任、合作等亲社会行为。不仅关注普通人群的社会行为及其背后的认知神经机制，同时也关注精神障碍人群社会功能受损的机制。研究手段包括：心理学实验设计，经济学博弈范式、计算建模、磁共振成像，眼动与瞳孔测量，脑电与脑磁图等。相关工作发表于Nature Communications, Current Biology, Neuroscience & Biobehavioral Reviews, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Psychoneuroendocrinology, Human Brain Mapping等期刊。其中关于儿童早期教育干预影响成年后社会决策的发现，被EurekAlert!等多家科学新闻媒体报道。曾担任Cerebral Cortex, Human Brain Mapping, Psychopshysiology等学术期刊的审稿人。入选第八届中国科协青年人才“托举工程”，获得第五届瀚翔青年科学家提名奖。

研究方向：社会认知与社会学习（社会规范、互惠行为、从众）、社会决策(信任建立与合作等)的计算过程与神经机制。

Qiang Luo

Fudan University

罗强，博士，副研究员，博士生导师，致力于发展多维度脑科学复杂数据整合分析新方法，及其在青少年脑发育和精神健康方面的应用研究，主持了国家自然科学基金3项、获国家发明专利1项、发表学术论文50余篇，包括精神疾病领域顶级期刊JAMA Psychiatry、Biological Psychiatry、American Journal of Psychiatry和脑影像数据分析与建模权威期刊 NeuroImage、PloS Computational Biology等，受邀为JAMA Psychiatry撰写特约评论；研究成果得到新华网、科学网、Science Daily、Daily Mail等国内外主流媒体报道。受邀担任英国伦敦国王学院高级访问讲师、上海市非线性科学研究会理事会副秘书长、Frontiers in Psychiatry期刊编辑，医学1区SCI期刊Psychological Medicine编委，并于2018年入选剑桥大学克莱尔学堂客座研究员。目前招收硕士、博士（统计学、心理学、生物医学、计算机科学）、并与复旦大学特聘教授、英国剑桥大学Trevor Robbins教授、Barbara Sahakian教授合作招收博士和博士后（认知神经科学、生物医学大数据挖掘、人工智能）。

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