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The Chemokine CCL2 Promotes Excitatory Synaptic Transmission in Hippocampal Neurons via GluA1 Subunit Trafficking

Neuroscience Bulletin 2024年第11期40卷 1649-1666页

作者： En Ji Yuanyuan Zhang Zhiqiang Li Lai Wei Zhaofa Wu Yulong Li Xiang Yu Tian‑Jia Song Institute of Neuroscience CAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai 200031China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Membrane Biology School of Life SciencesPeking‑Tsinghua Center for Life Sciencesand IDG/McGovern Institute for Brain ResearchPeking UniversityBeijing 100871China Institute of Genetics and Developmental Biology Chinese Academy of SciencesBeijing 100101China Shandong Provincial Key Medical and Health Laboratory of Psychiatric Genetics of Shandong Mental Health Center Shandong UniversityJinan 250014China

The CC chemokine ligand 2(CCL2,also known as MCP-1)and its cognate receptor CCR2 have wellcharacterized roles in ***2 has been previously shown to promote excitatory synaptic transmission and neuronal ***,the detailed molecular mechanism underlying this process remains largely *** cultured hippocampal neurons,CCL2 application rapidly upregulated surface expression of GluA1,in a CCR2-dependent manner,assayed using SEP-GluA1 live imaging,surface GluA1 antibody staining,and *** pharmacology and reporter assays,we further showed that CCL2 upregulated surface GluA1 expression primarily via Gαq-and CaMKII-dependent ***,using *** of lipopolysaccharide to induce neuroinflammation,we found upregulated phosphorylation of S831 and S845 sites on AMPA receptor subunit GluA1 in the hippocampus,an effect blocked in Ccr2−/−***,these results provide a mechanism through which CCL2,and other secreted molecules that signal through G-protein coupled receptors,can directly regulate synaptic transmission.

关键词： Synaptic transmission CCL2 MCP-1 CCR2 CaMKII AMPA receptor GluA1

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Sharing Weights in Shallow Layers via Rotation Group Equivariant Convolutions

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Machine intelligence Research 2022年第2期19卷 115-126页

作者： Zhiqiang Chen Ting-Bing Xu Jinpeng Li Huiguang He Research Center for Brain-inspired Intelligence Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Instrumentation and Optoelectronic Engineering Beihang UniversityBeijing 100191China Ningbo HwaMei Hospital University of Chinese Academy of SciencesNingbo 315012China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesBeijing 100190China

The convolution operation possesses the characteristic of translation group equivariance. To achieve more group equivariances, rotation group equivariant convolutions(RGEC) are proposed to acquire both translation and rotation group ***, previous work paid more attention to the number of parameters and usually ignored other resource costs. In this paper, we construct our networks without introducing extra resource costs. Specifically, a convolution kernel is rotated to different orientations for feature extractions of multiple channels. Meanwhile, much fewer kernels than previous works are used to ensure that the output channel does not increase. To further enhance the orthogonality of kernels in different orientations, we construct the non-maximum-suppression loss on the rotation dimension to suppress the other directions except the most activated one. Considering that the low-level-features benefit more from the rotational symmetry, we only share weights in the shallow layers(SWSL) via RGEC. Extensive experiments on multiple datasets(i.e., Image Net, CIFAR, and MNIST) demonstrate that SWSL can effectively benefit from the higher-degree weight sharing and improve the performances of various networks, including plain and Res Net architectures. Meanwhile, the convolutional kernels and parameters are much fewer(e.g., 75%, 87.5% fewer) in the shallow layers, and no extra computation costs are introduced.

关键词： Convolutional neural networks(CNNs) group equivariance higher-degree weight sharing parameter efficiency

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Single-cell profiling reveals Müller glia coordinate retinal intercellular communication during light/dark adaptation via thyroid hormone signaling

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Protein & Cell 2023年第8期14卷 603-617页

作者： Min Wei Yanping Sun Shouzhen Li Yunuo Chen Longfei Li Minghao Fang Ronghua Shi Dali Tong Jutao Chen Yuqian Ma Kun Qu Mei Zhang Tian Xue Division of Life Sciences and Medicine Department of OphthalmologyThe First Affiliated Hospital of USTCUniversity of Science and Technology of ChinaHefei 230026China Hefei National Research Center for Physical Sciences at the Microscale Neurodegenerative Disorder Research CenterCAS Key Laboratory of Brain Function and DiseaseUniversity of Science and Technology of ChinaHefei 230026China Division of Life Sciences and Medicine School of Life SciencesUniversity of Science and Technology of ChinaHefei 230026China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China Institute for Stem Cell and Regeneration Chinese Academy of SciencesBeijing 100864China

Light adaptation enables the vertebrate visual system to operate over a wide range of ambient *** of phototransduction in photoreceptors is considered a major mechanism underlying light ***,various types of neurons and glial cells exist in the retina,and whether and how all retinal cells interact to adapt to light/dark conditions at the cellular and molecular levels requires systematic ***,we utilized single-cell RNA sequencing to dissect retinal cell-type-specific transcriptomes during light/dark adaptation in *** results demonstrated that,in addition to photoreceptors,other retinal cell types also showed dynamic molecular changes and specifically enriched signaling pathways under light/dark ***,Müller glial cells(MGs)were identified as hub cells for intercellular interactions,displaying complex cell‒cell communication with other retinal ***,light increased the transcription of the deiodinase Dio2 in MGs,which converted thyroxine(T4)to active triiodothyronine(T3).Subsequently,light increased T3 levels and regulated mitochondrial respiration in retinal cells in response to light *** cones specifically express the thyroid hormone receptor Thrb,they responded to the increase in T3 by adjusting light *** of the expression of Dio2 specifically in MGs decreased the light responsive ability of *** results suggest that retinal cells display global transcriptional changes under light/dark adaptation and that MGs coordinate intercellular communication during light/dark adaptation via thyroid hormone signaling.

关键词： single cell Müller glial cells intercellular communication light/dark adaptation thyroid hormone signaling

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Inverse Reinforcement Learning with Attention-based Feature Extraction from Video Demonstrations

Inverse Reinforcement Learning with Attention-based Feature ...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Zhang, Weixin Lu, Tao Cai, Yinghao Wang, Shuo University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Cas Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China

ISBN: (纸本)9798350325706

The sparse reward problem makes the robot difficult to get effective feedback during learning skills with reinforcement learning, which will seriously affect the training efficiency. In this paper, we propose an inverse reinforcement learning (IRL) method to understand expert intent from video demonstrations and further provide dense rewards for robots to learn the task. We learn representations related to task progress by matching different videos in the embedding space based on their temporal alignment and incorporating attention mech-anisms to focus on the task-relevant features. Based on this, we design an efficient and robust dense reward function that enhances the efficiency of the reinforcement learning process. We validated our method in robotic manipulation tasks, and the experimental results showed that our approach exhibits faster convergence speed and stability and have the generalization ability in skill learning with video demonstrations from unseen embodiment. © 2023 IEEE.

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Neural circuit control of innate behaviors

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science China(Life sciences) 2022年第3期65卷 466-499页

作者： Wei Xiao Zhuo-Lei Jiao Esra Senol Jiwei Yao Miao Zhao Zheng-Dong Zhao Xiaowei Chen Peng Cao Yu Fu Zhihua Gao Wei L Shen Xiao-Hong Xu Department of Neurobiology and Department of Neurology of Second Affiliated Hospital NHC and CAMS Key Laboratory of Medical NeurobiologyZhejiang University School of Brain Science and Brain MedicineHangzhou 310058China 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 200031China Institute of Molecular and Cell Biology Agency for ScienceTechnology and Research(A*STAR)Singapore 138667Singapore Center for Neurointelligence School of MedicineChongqing UniversityChongqing 400030China National Institute of Biological Sciences Tsinghua Institute of Multidisciplinary Biomedical ResearchTsinghua UniversityBeijing 100084China Boston Children’s Hospital Harvard Medical SchoolBoston02115USA Brain Research Center and State Key Laboratory of Trauma Burnsand Combined InjuryThird Military Medical UniversityChongqing 400038China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai 200031China School of Life Science and Technology ShanghaiTech UniversityShanghai 201210China

All animals possess a plethora of innate behaviors that do not require extensive learning and are fundamental for their survival and *** the advent of newly-developed techniques such as viral tracing and optogenetic and chemogenetic tools,recent studies are gradually unraveling neural circuits underlying different innate ***,we summarize current development in our understanding of the neural circuits controlling predation,feeding,male-typical mating,and urination,highlighting the role of genetically defined neurons and their connections in sensory triggering,sensory to motor/motivation transformation,motor/motivation encoding during these different *** the way,we discuss possible mechanisms underlying binge-eating disorder and the pro-social effects of the neuropeptide oxytocin,elucidating the clinical relevance of studying neural circuits underlying essential innate ***,we discuss some exciting brain structures recurrently appearing in the regulation of different behaviors,which suggests both divergence and convergence in the neural encoding of specific innate *** forward,we emphasize the importance of multi-angle and cross-species dissections in delineating neural circuits that control innate behaviors.

关键词： innate behavior neural circuit feeding social behavior urination

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Is the brain Mechanism for Hierarchical Structure Building Universal Across Languages? An fMRI Study of Chinese and English

Is the Brain Mechanism for Hierarchical Structure Building U...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Zhang, Xiaohan Wang, Shaonan Lin, Nan Zong, Chengqing National Laboratory of Pattern Recognition Institute of Automation CAS China School of Artificial Intelligence University of Chinese Academy of Sciences China CAS Key Laboratory of Behavioural Sciences Institute of Psychology China Department of Psychology University of Chinese Academy of Sciences China CAS Center for Excellence in Brain Science and Intelligence Technology China

Evidence from psycholinguistic studies suggests that the human brain builds a hierarchical syntactic structure during language comprehension. However, it is still unknown whether the neural basis of such structures is universal across languages. In this paper, we first analyze the differences in language structure between two diverse languages: Chinese and English. By computing the working memory requirements when applying parsing strategies to different language structures, we find that top-down parsing generates less memory load for the right-branching English and bottom-up parsing is less memory-demanding for Chinese. Then we use functional magnetic resonance imaging (fMRI) to investigate whether the brain has different syntactic adaptation strategies in processing Chinese and English. Specifically, for both Chinese and English, we extract predictors from the implementations of different parsing strategies, i.e., bottom-up and top-down. Then, these predictors are separately associated with fMRI signals. Results show that for Chinese and English, the brain utilizes bottom-up and top-down parsing strategies separately. These results suggest that the brain adopts parsing strategies with less memory load according to different language structures. © 2022 Association for Computational Linguistics.

关键词： Syntactics

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DTLLM-VLT: Diverse Text Generation for Visual Language Tracking Based on LLM

DTLLM-VLT: Diverse Text Generation for Visual Language Track...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Xuchen Li Xiaokun Feng Shiyu Hu Meiqi Wu Dailing Zhang Jing Zhang Kaiqi Huang CRISE Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Visual Language Tracking (VLT) enhances single object tracking (SOT) by integrating natural language descriptions from a video, for the precise tracking of a specified object. By leveraging high-level semantic information, VLT guides object tracking, alleviating the constraints associated with relying on a visual modality. Nevertheless, most VLT benchmarks are annotated in a single granularity and lack a coherent semantic framework to provide scientific guidance. Moreover, coordinating human annotators for high-quality annotations is laborious and time-consuming. To address these challenges, we introduce DTLLM-VLT, which automatically generates extensive and multi-granularity text to enhance environmental diversity. (1) DTLLM-VLT generates scientific and multi-granularity text descriptions using a cohesive prompt framework. Its succinct and highly adaptable design allows seamless integration into various visual tracking benchmarks. (2) We select three prominent benchmarks to deploy our approach: short-term tracking, long-term tracking, and global instance tracking. We offer four granularity combinations for these benchmarks, considering the extent and density of semantic information, thereby showcasing the practicality and versatility of DTLLM-VLT. (3) We conduct comparative experiments on VLT benchmarks with different text granularities, evaluating and analyzing the impact of diverse text on tracking performance. Conclusionally, this work leverages LLM to provide multi-granularity semantic information for VLT task from efficient and diverse perspectives, enabling fine-grained evaluation of multi-modal trackers. In the future, we believe this work can be extended to more datasets to support vision datasets understanding.

关键词： Visualization Computer vision Annotations Conferences Semantics Natural languages Benchmark testing

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cascaded Decoding and Multi-Stage Inference for Spatio-Temporal Video Grounding 4

Cascaded Decoding and Multi-Stage Inference for Spatio-Tempo...

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4th Person in Context Workshop, PIC 2022

作者： Yang, Li Wu, Peixuan Yuan, Chunfeng Li, Bing Hu, Weiming Institute of Automation Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China

ISBN: (纸本)9781450394895

Human-centric spatio-Temporal video grounding (HC-STVG) is a challenging task that aims to localize the spatio-Temporal tube of the target person in a video based on a natural language description. In this report, we present our approach for this challenging HC-STVG task. Specifically, based on the TubeDETR framework, we propose two cascaded decoders to decouple spatial and temporal grounding, which allows the model to capture respective favorable features for these two grounding subtasks. We also devise a multi-stage inference strategy to reason about the target in a coarse-To-fine manner and thereby produce more precise grounding results for the target. To further improve accuracy, we propose a model ensemble strategy that incorporates the results of models with better performance in spatial or temporal grounding. We validated the effectiveness of our proposed method on the HC-STVG 2.0 dataset and won second place in the HC-STVG track of the 4th Person in Context (PIC) workshop at ACM MM 2022. © 2022 ACM.

关键词： Decoding

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A Learning-based Acceleration Framework for Transient Hemodynamic Simulations

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Procedia Computer science 2024年 250卷 136-142页

作者： Qin-Yi Zhang Xiao-Hu Zhou Xiao-Liang Xie Shi-Qi Liu Zhen-Qiu Feng Mei-Jiang Gui Hao Li Tian-Yu Xiang De-Xing Huang Zeng-Guang Hou The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing 100190 China

Numerical simulations of transient hemodynamics have increasingly become an important assessment for vascular disease. However, the procedure often requires cumbersome steps and high computational costs. To tackle above issues, a novel learning-based acceleration framework is proposed to simulate transient hemodynamics of abdominal aortic aneurysms (AAA). This framework innovatively combines static spatial features with dynamic temporal features to accurately simulate blood pressure at each timestep. PointNet++ is employed to extract hierarchical spatial features from the AAA point clouds. Subsequently, these features are integrated with boundary conditions for learning temporal dynamics using Transformer. The proposed framework is trained and evaluated on a hemodynamic dataset comprising 18 patients, achieving a mean absolute error of 80.87 Pa in blood pressure simulations. These promising results indicate the potential of the framework to facilitate AAA diagnosis efficiently.

关键词： Hemodynamics Computational Fluid Dynamics Point Cloud Abdominal Aortic Aneurysms

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PeLK: Parameter-efficient Large Kernel ConvNets with Peripheral Convolution

arXiv

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arXiv 2024年

作者： Chen, Honghao Chu, Xiangxiang Ren, Yongjian Zhao, Xin Huang, Kaiqi CRISE Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Meituan China CAS Center for Excellence in Brain Science and Intelligence Technology China

Recently, some large kernel convnets strike back with appealing performance and efficiency. However, given the square complexity of convolution, scaling up kernels can bring about an enormous amount of parameters and the proliferated parameters can induce severe optimization problem. Due to these issues, current CNNs compromise to scale up to 51 × 51 in the form of stripe convolution (i.e., 51 × 5 + 5 × 51) and start to saturate as the kernel size continues growing. In this paper, we delve into addressing these vital issues and explore whether we can continue scaling up kernels for more performance gains. Inspired by human vision, we propose a human-like peripheral convolution that efficiently reduces over 90% parameter count of dense grid convolution through parameter sharing, and manage to scale up kernel size to extremely large. Our peripheral convolution behaves highly similar to human, reducing the complexity of convolution from O(K2) to O(logK) without backfiring performance. Built on this, we propose Parameter-efficient Large Kernel Network (PeLK). Our PeLK outperforms modern vision Transformers and ConvNet architectures like Swin, ConvNeXt, RepLKNet and SLaK on various vision tasks including ImageNet classification, semantic segmentation on ADE20K and object detection on MS COCO. For the first time, we successfully scale up the kernel size of CNNs to an unprecedented 101 × 101 and demonstrate consistent improvements. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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