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The Structure and Function of Glial Networks:Beyond the Neuronal Connections

Neuroscience Bulletin 2023年第3期39卷 531-540页

作者： Hai‑Rong Peng Yu-Kai Zhang Jia-Wei Zhou Institute of Neuroscience State Key Laboratory of NeuroscienceCAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai 200031China School of Future Technology University of Chinese Academy of SciencesBeijing 100049China

Glial cells,consisting of astrocytes,oligodendrocyte lineage cells,and microglia,account for>50% of the total number of cells in the mammalian *** play key roles in the modulation of various brain activities under physiological and pathological *** the typical morphological features and characteristic functions of these cells are well described,the organization of interconnections of the different glial cell populations and their impact on the healthy and diseased brain is not completely *** these processes remains a profound *** evidence suggests that glial cells can form highly complex interconnections with each *** astroglial network has been well *** and microglia may also contribute to the formation of glial networks under various *** this review,we discuss the structure and function of glial networks and their pathological relevance to central nervous system *** also highlight opportunities for future research on the glial connectome.

关键词： Glia network Gap junction Calcium coupling CNS diseases

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Prioritized Tasks Mining for Multi-Task Cooperative Multi-Agent Reinforcement Learning 22

Prioritized Tasks Mining for Multi-Task Cooperative Multi-Ag...

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22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Yu, Yang Yin, Qiyue Zhang, Junge Huang, Kaiqi School of Artificial Intelligence University of Chinese Academy of Sciences Institute of Automation Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China

Multi-task learning improves data efficiency in cooperative multiagent reinforcement learning, since agents can learn multiple related tasks simultaneously and the cooperation knowledge in a task can be utilized by others. However, existing methods mainly learn multiple cooperation tasks uniformly, regardless of their complexity and significance. In this paper, we propose a new framework called Prioritized Tasks Mining (PTM) for multi-task cooperation problems, which helps agents to identify and mine higher priority cooperation tasks, so as to learn more effective coordinated strategies for multiple cooperation tasks. Specially, agents will use the hindsight during training to identify the priority of different tasks, and make an exploration and exploitation in higher priority cooperative tasks to mine more sophisticated coordinated strategies. We evaluate PTM in challenging multi-task StarCraft micromanagement games with different scales, and results demonstrate that our method consistently outperforms all strong baselines. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Reinforcement learning

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Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 722-736页

作者： Da Yu Mingyi Zhang Mantian Li Fusheng Zha Junge Zhang Lining Sun Kaiqi Huang the State Key Laboratory of Robotics and System Harbin Institute of Technology(HIT)Harbin 150080China Center for Research on Intelligent System and Engineering Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Center for Research on Intelligent System and Engineering Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190the School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing 100049 the CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China IEEE

Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter *** this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new ***,prior knowledge is unavailable during training and *** works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory *** better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous ***,besides the previous raw samples,we store the corresponding logits and features in the ***,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the *** the same number of samples for replay,our method can use more past knowledge to prevent *** conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic *** further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.

关键词： Catastrophic forgetting class-incremental learning continual learning(CL) experience replay

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Extending generalized unsupervised manifold alignment

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science China(Information sciences) 2022年第7期65卷 139-156页

作者： Xiaoyi YIN Zhen CUI Hong CHANG Bingpeng MA Shiguang SHAN Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science and Engineering Nanjing University of Science and Technology CAS Center for Excellence in Brain Science and Intelligence Technology

Building connections between different data sets is a fundamental task in machine learning and related application community. With proper manifold alignment, the correspondences between data sets will assist us with comprehensive study of data processes and analyses. Despite the several progresses in semi-supervised and unsupervised scenarios, potent manifold alignment methods in generalized and realistic circumstances remain in absence. Besides, theretofore unsupervised algorithms seldom prove themselves mathematically. In this paper, we devise an efficient method to properly solve the unsupervised manifold alignment problem and denominate it as extending generalized unsupervised manifold alignment(EGUMA)method. More specifically, an explicit relaxed integer programming method is adopted to solve the unsupervised manifold alignment problem, which reconciles three factors covering the updated local structure matching, the the feature comparability and geometric preservation. An additional effort is retained on extending the Frank Wolfe algorithm to tacking our optimization problem. Besides our previous endeavors we adopt a new strategy for neighborhood discovery in the manifolds. The main advantages over previous methods accommodate(1) simultaneous alignment and discovery of manifolds;(2) complete unsupervised learning structure without any prerequisite correspondence;(3) more concise local geometry for the embedding space;(4) efficient alternative optimization;(5) strict mathematical analysis on the convergence and efficiency issues. Experiments on real-world applications verify the high accuracy and efficiency of our proposed method.

关键词： unsupervised manifold alignment relaxed integer programming Frank Wolfe algorithm

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CD2AP deficiency aggravates Alzheimer’s disease phenotypes and pathology through p38 MAPK activation

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Translational Neurodegeneration 2024年第1期13卷 93-113页

作者： Yan-Yan Xue Zhe-Sheng Zhang Rong-Rong Lin Hui-Fen Huang Ke-Qing Zhu Dian-Fu Chen Zhi-Ying Wu Qing-Qing Tao Department of Neurology The Second Affiliated HospitalZhejiang University School of Medicine and Liangzhu Laboratory88 Jiefang RoadHangzhou 310009China National Health and Disease Human Brain Tissue Resource Center and Department of Pathology School of MedicineZhejiang UniversityHangzhou 310058China MOE Frontier Science Center for Brain Science and Brain-Machine Integration School of Brain Science and Brain MedicineZhejiang UniversityHangzhou 310058China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China

Background Alzheimer’s disease(AD)is the most common form of neurodegenerative disorder,which is characterized by a decline in cognitive ***-wide association and clinicopathological studies have demonstrated that the CD2-associated protein(CD2AP)gene is one of the most important genetic risk factors for ***,the precise mechanisms by which CD2AP is linked to AD pathogenesis remain *** The spatiotemporal expression pattern of CD2AP was ***,we generated and characterized an APP/PS1 mouse model with neuron-specific Cd2ap deletion,using immunoblotting,immunofluorescence,enzyme-linked immunosorbent assay,electrophysiology and behavioral ***,we established a stable CD2AP-knockdown SH-SY5Y cell line to further elucidate the specific molecular mechanisms by which CD2AP contributes to AD ***,the APP/PS1 mice with neuron-specific Cd2ap deletion were treated with an inhibitor targeting the pathway identified above to further validate our *** CD2AP is widely expressed in various regions of the mouse brain,with predominant expression in neurons and vascular endothelial *** APP/PS1 mice,neuronal knockout of Cd2ap significantly aggravated tau pathology,synaptic impairments and cognitive ***,the knockout of Cd2ap activated p38 mitogen-activated protein kinase(MAPK)signaling,which contributed to increased tau phosphorylation,synaptic injury,neuronal apoptosis and cognitive ***,the phenotypes of neuronal Cd2ap knockout were ameliorated by a p38 MAPK *** Our study presents the first in vivo evidence that CD2AP deficiency exacerbates the phenotypes and pathology of AD through the p38 MAPK pathway,identifying CD2AP/p38 MAPK as promising therapeutic targets for AD.

关键词： Alzheimer’s disease CD2AP P38 MAPK Synaptic injury Tau

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Can You Feel the Love Tonight: A Dipeptidergic Circuit for Pleasant Touch

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Neuroscience Bulletin 2022年第8期38卷 966-968页

作者： Jing-Jing Yan Wen Zhang Xiao-Hong Xu Institute of Neuroscience State Key Laboratory of NeuroscienceCAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China

Soft and gentle touches such as the maternal caressing of infants foster a sense of well-being,promote social bonding,and have long-lasting impacts on the brain and *** Yu's group recently identified a dipeptidergic excitatory circuit,from tachykinin 1(Tacl)-expressing neurons in the ventrolateral periaqueductal gray(/vIPAG)to oxytocin neurons in the paraventricular hypothalamus(PVH),that mediates the enduring effects of gentle touch on social behaviors.

关键词： peptidergic gentle hypothalamus

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An interview with Mingjie Zhang: phase separation in biological systems

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National science Review 2021年第9期8卷 192-195页

作者： Weijie Zhao Mu-ming Poo NSR the CAS Center for Excellence in Brain Science and Intelligence Technology

Mingjie Zhang is a distinguished structural biologist whose research interest is the protein assemblies in cells, particularly those at the synapse, where two neurons meet and transmit nervous impulses from one to the other. In recent years, his group discovered the phenomenon of phase separation in both the pre-and post-synaptic sites of neurons and elucidated its regulation and physiological function. In this interview, NSR Executive Editor-in-Chief Mu-ming Poo talks with Prof. Zhang on the fast-developing research area of biological phase separation, as well as Zhang’s new role as the Founding Dean of the School of Life sciences at Southern University of science and technology(SUSTech) in Shenzhen.

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AI in Human-computer Gaming:Techniques,Challenges and Opportunities

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Machine intelligence Research 2023年第3期20卷 299-317页

作者： Qi-Yue Yin Jun Yang Kai-Qi Huang Mei-Jing Zhao Wan-Cheng Ni Bin Liang Yan Huang Shu Wu Liang Wang Institute of Automation Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Automation Tsinghua UniversityBeijing 100084China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesBeijing 100190China

With the breakthrough of AlphaGo,human-computer gaming AI has ushered in a big explosion,attracting more and more researchers all over the *** a recognized standard for testing artificial intelligence,various human-computer gaming AI systems(AIs)have been developed,such as Libratus,OpenAI Five,and AlphaStar,which beat professional human *** rapid development of human-computer gaming AIs indicates a big step for decision-making intelligence,and it seems that current techniques can handle very complex human-computer ***,one natural question arises:What are the possible challenges of current techniques in human-computer gaming and what are the future trends?To answer the above question,in this paper,we survey recent successful game AIs,covering board game AIs,card game AIs,first-person shooting game AIs,and real-time strategy game *** this survey,we 1)compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human-level AIs;2)summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer games;3)raise the challenges or drawbacks of current techniques in the successful AIs;and 4)try to point out future trends in human-computer gaming ***,we hope that this brief review can provide an introduction for beginners and inspire insight for researchers in the field of AI in human-computer gaming.

关键词： Human-computer gaming AI intelligent decision making deep reinforcement learning self-play

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Sliding mode-based adaptive tube model predictive control for robotic manipulators with model uncertainty and state constraints

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Control Theory and technology 2023年第3期21卷 334-351页

作者： Erlong Kang Yang Liu Hong Qiao State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Research and Application for Robotic Intelligence of Hand-Eye-Brain Interaction Beijing 100190China The School of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China University of Chinese Academy of Sciences Beijing 100049China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China

In this paper,the optimal tracking control for robotic manipulators with state constraints and uncertain dynamics is investigated,and a sliding mode-based adaptive tube model predictive control method is ***,utilizing the high-order fully actuated system approach,the nominal model of the robotic manipulator is constructed as the predictive *** on the nominal model,a nominal model predictive controller with the sliding mode is designed,which relaxes the terminal constraints,and realizes the accurate and stable tracking of the desired trajectory by the nominal ***,an auxiliary controller based on the node-adaptive neural networks is constructed to dynamically compensate nonlinear uncertain dynamics of the robotic ***,the estimation deviation between the nominal and actual states is limited to the tube invariant *** the same time,the recursive feasibility of nominal model predictive control is verified,and the ultimately uniformly boundedness of all variables is proved according to the Lyapunov ***,experiments show that the robotic manipulator can achieve fast and efficient trajectory tracking under the action of the proposed method.

关键词： Tube-based model predictive control-Robotic manipulator Sliding mode Node-adaptive neural networks Model uncertainty

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Identification of de novo Mutations in the Chinese Autism Spectrum Disorder Cohort via Whole-Exome Sequencing Unveils brain Regions Implicated in Autism

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Neuroscience Bulletin 2023年第10期39卷 1469-1480页

作者： Bo Yuan Mengdi Wang Xinran Wu Peipei Cheng Ran Zhang Ran Zhang Shunying Yu Jie Zhang Yasong Du Xiaoqun Wang Zilong Qiu Songjiang Research Institute Songjiang HospitalShanghai Jiao Tong University School of MedicineShanghai 201600China Shanghai Mental Health Center Shanghai Jiao Tong University School of MedicineShanghai 200032China Institute of Neuroscience State Key Laboratory of NeuroscienceCAS Center for Excellence in Brain Science and Intelligence TechnologyShanghai Center for Brain Science and Brain-Inspired Intelligence TechnologyChinese Academy of SciencesShanghai 200032China Beijing Normal University Beijing 100875China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China

Autism spectrum disorder(ASD)is a highly heritable neurodevelopmental disorder characterized by deficits in social interactions and repetitive *** hundreds of ASD risk genes,implicated in synaptic formation and transcriptional regulation,have been identified through human genetic studies,the East Asian ASD cohorts are still under-represented in genome-wide genetic ***,we applied whole-exome sequencing to 369 ASD trios including probands and unaffected parents of Chinese *** a joint-calling analytical pipeline based on GATK toolkits,we identified numerous de novo mutations including 55 high-impact variants and 165 moderate-impact variants,as well as de novo copy number variations containing known ASD-related ***,combined with single-cell sequencing data from the developing human brain,we found that the expression of genes with de novo mutations was specifically enriched in the pre-,post-central gyrus(PRC,PC)and banks of the superior temporal(BST)regions in the human *** further analyzing the brain imaging data with ASD and healthy controls,we found that the gray volume of the right BST in ASD patients was significantly decreased compared to healthy controls,suggesting the potential structural deficits associated with ***,we found a decrease in the seed-based functional connectivity between BST/PC/PRC and sensory areas,the insula,as well as the frontal lobes in ASD *** work indicated that combinatorial analysis with genome-wide screening,single-cell sequencing,and brain imaging data reveal the brain regions contributing to the etiology of ASD.

关键词： Autism Cohorts Whole-exome sequencing Single-cell sequencing

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