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Sustained antidepressant effect of ketamine through NMDAR trapping in the LHb (vol 622, pg 802, 2023)

NATURE 2023年第7988期623卷 E11-E11页

作者： Ma, Shuangshuang Chen, Min Jiang, Yihao Xiang, Xinkuan Wang, Shiqi Wu, Zuohang Li, Shuo Cui, Yihui Wang, Junying Zhu, Yanqing Zhang, Yan Ma, Huan Duan, Shumin Li, Haohong Yang, Yan Lingle, Christopher J. Hu, Hailan Department of Psychiatry and International Institutes of Medicine The Fourth Affiliated Hospital Zhejiang University School of Medicine Yiwu China Nanhu Brain–Computer Interface Institute MOE Frontier Science Center for Brain Science and Brain–Machine Integration State Key Laboratory of Brain–Machine Intelligence New Cornerstone Science Laboratory Zhejiang University Hangzhou China Department of Affiliated Mental Health Center and Hangzhou Seventh People’s Hospital and School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou China Department of Anesthesiology Washington University School of Medicine St Louis MO USA

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Morphological disruption and visual tuning alterations in the primary visual cortex in glaucoma(DBA/2J)mice

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Neural Regeneration Research 2024年第1期19卷 220-225页

作者： Yin Yang Zhaoxi Yang Maoxia Lv Ang Jia Junjun Li Baitao Liao Jing’an Chen Zhengzheng Wu Yi Shi Yang Xia Dezhong Yao Ke Chen Department of Ophthalmology Sichuan Provincial People’s HospitalMedical SchoolUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroInformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina Research Unit of NeuroInformation Chinese Academy of Medical SciencesChengduSichuan ProvinceChina Institute of Basic Medical Sciences Chinese Academy of Medical Sciences School of Basic Medicine Peking Union Medical CollegeBeijingChina Health Management Center Sichuan Provincial Key Laboratory for Human Disease Gene StudySichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina Research Unit for Blindness Prevention of Chinese Academy of Medical Sciences(2019RU026) Sichuan Academy of Medical SciencesChengduSichuan ProvinceChina School of Electrical Engineering Zhengzhou UniversityZhengzhouHenan ProvinceChina

Glaucoma is a leading cause of irreve rsible blindness wo rldwide,and previous studies have shown that,in addition to affecting the eyes,it also causes abnormalities in the ***,it is not yet clear how the primary visual cortex(V1)is altered in *** study used DBA/2J mice as a model for spontaneous secondary *** aim of the study was to compare the electrophysiological and histomorphological chara cteristics of neurons in the V1between 9-month-old DBA/2J mice and age-matched C57BL/6J *** conducted single-unit recordings in the V1 of light-anesthetized mice to measure the visually induced responses,including single-unit spiking and gamma band *** morphology of layerⅡ/Ⅲneurons was determined by neuronal nuclear antigen staining and Nissl staining of brain tissue ***-seven neurons from eight DBA/2J mice and eighty-one neurons from eight C57BL/6J mice were *** with the C57BL/6J group,V1 neurons in the DBA/2J group exhibited weaker visual tuning and impaired spatial *** r,fewer neuro ns were observed in the V1 of DBA/2J mice compared with C57BL/6J *** findings suggest that DBA/2J mice have fewer neurons in the VI compared with C57BL/6J mice,and that these neurons have impaired visual *** findings provide a better understanding of the pathological changes that occur in V1 neuron function and morphology in the DBA/2J mouse *** study might offer some innovative perspectives regarding the treatment of glaucoma.

关键词： DBA/2J degeneration gamma band oscillations glaucoma primary visual cortex(V1) retina single-unit recording tuning curve

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Dear-DIA^(XMBD): Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

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Research 2024年第1期6卷 707-720页

作者： Qingzu He Chuan-Qi Zhong Xiang Li Huan Guo Yiming Li Mingxuan Gao Rongshan Yu Xianming Liu Fangfei Zhang Donghui Guo Fangfu Ye Tiannan Guo Jianwei Shuai Jiahuai Han Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China Oujiang Laboratory(Zhejiang Lab for Regenerative Medicine Vision and Brain Health)and Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang 325001China School of Life Sciences Xiamen UniversityXiamen 361102China State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkXiamen 361102China Department of Computer Science Xiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine School of MedicineXiamen UniversityXiamen 361102China Bruker(Beijing)Scientific Technology Co.Ltd. BeijingChina Westlake Laboratory of Life Sciences and Biomedicine Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University18 Shilongshan RoadHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced Study18 Shilongshan RoadHangzhou 310024China Westlake Omics Ltd. Yunmeng Road 1HangzhouChina Department of Electronic Engineering Xiamen UniversityXiamen 361005China

Data-independent acquisition(DIA)technology for protein identification from mass spectrometry and related algorithms is developing *** spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising *** this paper,we proposed an untargeted analysis method,Dear-DIA^(XMBD),for direct analysis of DIA ***-DIA^(XMBD) first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms,then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes,and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and *** show that Dear-DIA^(XMBD) performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms.

关键词： Deep Auto Independent

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Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

arXiv

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arXiv 2025年

作者： Ma, Chenxiang Chen, Xinyi Li, Yanchen Yang, Qu Wu, Yujie Li, Guoqi Pan, Gang Tang, Huajin Tan, Kay Chen Wu, Jibin Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Institute of Automation Chinese Academy of Sciences Beijing100045 China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou310027 China

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Point, Segment and Count: A Generalized Framework for Object Counting

arXiv

引用

arXiv 2023年

作者： Huang, Zhizhong Dai, Mingliang Zhang, Yi Zhang, Junping Shan, Hongming Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China School of Cyber Science and Engineering Sichuan University Chengdu610065 China Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Shanghai200433 China

Class-agnostic object counting aims to count all objects in an image with respect to example boxes or class names, a.k.a few-shot and zero-shot counting. In this paper, we propose a generalized framework for both few-shot and zero-shot object counting based on detection. Our framework combines the superior advantages of two foundation models without compromising their zero-shot capability: (i) SAM to segment all possible objects as mask proposals, and (ii) CLIP to classify proposals to obtain accurate object counts. However, this strategy meets the obstacles of efficiency overhead and the small crowded objects that cannot be localized and distinguished. To address these issues, our framework, termed PseCo, follows three steps: point, segment, and count. Specifically, we first propose a class-agnostic object localization to provide accurate but least point prompts for SAM, which consequently not only reduces computation costs but also avoids missing small objects. Furthermore, we propose a generalized object classification that leverages CLIP image/text embeddings as the classifier, following a hierarchical knowledge distillation to obtain discriminative classifications among hierarchical mask proposals. Extensive experimental results on FSC-147, COCO, and LVIS demonstrate that PseCo achieves state-of-the-art performance in both few-shot/zero-shot object counting/detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Learning-Based Problem Reduction for Large-Scale Uncapacitated Facility Location Problems

Learning-Based Problem Reduction for Large-Scale Uncapacitat...

引用

Congress on Evolutionary Computation

作者： Shuaixiang Zhang Yixuan Yang Hao Tong Xin Yao Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Computing and Decision Sciences Lingnan University Hong Kong SAR China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

The uncapacitated facility location problem (UFLP) is a well-known combinational optimization problem, attracting numerous heuristic and meta-heuristic methods. However, these effective algorithms still encounter challenges when solving large-scale UFLP instances. To deal with the thousands of decision variables, one possible approach is to predict and remove unimportant facilities and thus decreases the dimensionality of problem instances. This paper explores the problem reduction of large-scale instances of UFLP. We propose suitable features of facilities to construct machine learning models for UFLP by learning from small instances. Based on this model, we introduce a novel problem reduction strategy-based optimization framework, and apply it to large instances. Through comprehen-sive experiments, we show that the proposed problem reduction strategy can effectively transform the UFLP instances to smaller ones. The performance of existing solution methods can be significantly enhanced, especially on large-scale instances. Under the proposed framework, we also show the generalization abilities of our models, which can be improved further.

关键词： Dimensionality reduction Metaheuristics Training data Transforms Evolutionary computation Benchmark testing Prediction algorithms

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Data-Driven Output Containment Control of Heterogeneous Multiagent Systems: A Hierarchical Scheme 63

Data-Driven Output Containment Control of Heterogeneous Mult...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Sader, Malika Li, Wenyu Yin, Yanhui Li, Zhongmei Huang, Dexian Liu, Zhongxin He, Xiao Shang, Chao Tsinghua University Department of Automation Beijing100084 China Nankai University Coll. of Artif. Intell. Tianjin Key Lab. of Interventional Brain-Comp. Interface and Intelligent Rehab. Tianjin300350 China North-East Electric Power University School of Automation Engineering Jilin132000 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Tsinghua University Department of Automation Beijing National Research Center for Information Science and Technology Beijing100084 China

ISBN: (纸本)9798350316339

In this article, we propose a data-driven solution to the output containment control problem of multiagent systems characterized by heterogeneous and unknown dynamics. The proposed data-driven scheme is hierarchical, which includes a network layer and a physical layer, bypassing the traditional modeling exercise and eliminating the need for explicit state-space models. In the network layer, a fully distributed observer without knowing the dynamics of leaders is designed to generate a containment trajectory. A data-driven approach based on data sampled from an auxiliary system is developed to overcome the dependence on the explicit state-space models for design of control gains. This paves the way for deriving a datadriven solution to the regulator equation, which is derived by employing the data informativity condition and relevant data in the physical layer. The results demonstrate that the closedloop system is asymptotically stable, and the regulated output containment converges to zero. Compared to generic modelbased hierarchical schemes, the assumptions that the system matrices are completely known and homogeneous on system dynamics can be relaxed. © 2024 IEEE.

关键词： Hierarchical systems

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Tuning In to Neural Encoding: Linking Human brain and Artificial Supervised Representations of Language

arXiv

引用

arXiv 2023年

作者： Sun, Jingyuan Zhang, Xiaohan Moens, Marie-Francine Department of Computer Science KU Leuven Belgium Institute of Neuroscience Key Laboratory of Primate Neurobiology CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China

To understand the algorithm that supports the human brain’s language representation, previous research has attempted to predict neural responses to linguistic stimuli using embeddings generated by artificial neural networks (ANNs), a process known as neural encoding. However, most of these studies have focused on probing neural representations of Germanic languages, such as English, with unsupervised ANNs. In this paper, we propose to bridge the gap between human brain and supervised ANN representations of the Chinese language. Specifically, we investigate how task tuning influences a pretained Transformer for neural encoding and which tasks lead to the best encoding performances. We generate supervised representations on eight Natural Language Understanding (NLU) tasks using prompt-tuning, a technique that is seldom explored in neural encoding for language. We demonstrate that prompt-tuning yields representations that better predict neural responses to Chinese stimuli than traditional fine-tuning on four tasks. Furthermore, we discover that tasks that require a fine-grained processing of concepts and entities lead to representations that are most predictive of brain activation patterns. Additionally, we reveal that the proportion of tuned parameters highly influences the neural encoding performance of fine-tuned models. Overall, our experimental findings could help us better understand the relationship between supervised artificial and brain language representations. Copyright © 2023, The Authors. All rights reserved.

关键词： Neural networks

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Multi-Modal brain Network Fusion for Intelligent Diagnostic Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Shengrong Zhu, Qi Sun, Liang Ma, Kai Ji, Yixin Qi, Shile Zhang, Daoqiang Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing211106 China University of Aeronautics and Astronautics Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing211106 China Jiangsu University of Science and Technology School of Computer Jiangsu Province Zhenjiang212100 China

Embedding multi-modal brain network analysis technology into consumer electronics, such as smart wearables, helps enable early intelligent diagnosis of brain diseases. Recent studies confirm that the functional-structural coupling in certain regions is more tightly correlated than in others. However, existing multi-modal methods often directly fuse functional brain networks (FBN) and structural brain networks (SBN), ignoring the regional heterogeneity between them. Additionally, identity information encoded in brain networks may interfere with disease diagnosis. In this paper, we develop a multi-modal brain network fusion method with regional heterogeneity constraints, and design a feature decoupling module to alleviate disease-irrelevant information. Specifically, we first divide FBN and SBN into multiple subnetworks, and introduce penalty weights to reduce the communication cost between cross-modal brain regions within the subnetwork while increasing the cost between different subnetworks. Then, under the regional heterogeneity constraints, we adopt optimal transport to simulate the transfer of brain region hubness from FBN to SBN, thereby effectively integrating the complex cross-modal interactions. Furthermore, we design a feature decoupling module to suppress ineffective features and enhance the discrimination between modality-specific features and multi-modal features. Experimental results show that the proposed method has promising performance and can identify multi-modal biomarkers for brain disease diagnosis. © 1975-2011 IEEE.

关键词： Wearable technology

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Video Corpus Moment Retrieval with Query-specific Context Learning and Progressive Localization

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5659-5670页

作者： Zhang, Long Song, Peipei Duan, Zhangling Wang, Shuo Chang, Xiaojun Yang, Xun School of Information Science and Technology Hefei230026 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230026 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Department of Computer Vision Abu Dhabi United Arab Emirates University of Science and Technology of China MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition China

Video corpus moment retrieval (VCMR) aims to retrieve a moment from a large corpus of untrimmed videos corresponding to a given language query. However, existing methods often fall short due to their reliance on simple cross-modal attention mechanisms and one-stop localization, which fail to handle the complex multimodal information and large search space effectively. To address these challenges, we propose a novel VCMR method with Query-specific Context Learning and Progressive Localization (QCLPL). First, we construct query-specific multimodal contexts that capture complementary and consistent semantics across subtitles and frames, ensuring informative and efficient context building. We further introduce a semantic contrastive loss to refine these multimodal contexts, filtering out query-irrelevant information. Additionally, we introduce a progressive localization strategy that transforms the moment localization task into a two-stage process. By classifying frames into foreground and background regions, we present a simplified binary classification problem before boundary prediction, constrained by a region-aware loss. This progressive approach leverages region priors to improve subsequent moment localization. Extensive experiments on the TVR and DiDeMo datasets demonstrate that our method significantly outperforms existing approaches, setting a new state of the art for VCMR. © 1991-2012 IEEE.

关键词： Structured Query Language

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