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Foxc1b regulates brain pericyte proliferation in zebrafish larvae

Journal of Genetics and Genomics 2024年第11期51卷 1334-1337页

作者： Huaxing Zi Xiaolan Peng Le Sun Hongyu Li Xiulian Shen Minjia Chen Qiusui Deng Ye Hua Jiulin Du Jia Li Institute of Neuroscience State Key Laboratory of NeuroscienceCAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai 200031China Dongguan Innovation Institute Guangdong Medical UniversityDongguanGuangdong 523808China University of Chinese Academy of Sciences Beijing 100049China School of Life Science and Technology ShanghaiTech UniversityShanghai 200031China

Pericytes,the mural cells enveloping small vessels,play a crucial role in maintaining the integrity of the blood-brain barrier(BBB)(Armulik et al.,2010;Daneman et al.,2010).Their loss leads to significant defects in BBB function,characterized by increased transcytosis across endothelial cells(ECs),diminished deposition of the cerebrovascular basement membrane,and compromised differentiation of astrocyte end-feet(Armulik et al.,2010;Daneman et al.,2010).

关键词： cytes basement

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Enhanced Beta2-band Oscillations Denote Auditory Hallucination in Schizophrenia Patients and a Monkey Model of Psychosis

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Neuroscience Bulletin 2024年第3期40卷 325-338页

作者： Shuo Ma Tianzhen Chen Wenjun Jia Jie Liu Shihan Ding Puzhe Li Hong Gan Dapeng Zhang Shuxin Shao Mu-Ming Poo Min Zhao Bomin Sun Jian Jiang Department of Neurosurgery Ruijin HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai200020China Shanghai Mental Health Center Shanghai Jiao Tong University School of MedicineShanghai200030China Shanghai Center for Brain Science and Brain-inspired Technology Lingang LaboratoryShanghai201602China University of California at Berkeley BerkeleyCA94720USA School of Life Science and Technology ShanghaiTech UniversityShanghai201210China Institute of Neuroscience CAS Center for Excellence in Brain Science and Intelligence TechnologyCAS Key Laboratory of Primate NeurobiologyState Key Laboratory of NeuroscienceChinese Academy of SciencesShanghai200031China Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology Lingang LaboratoryShanghai201210China Fuyang Third People’s Hospital Fuyang Mental Health CenterFuyang236052China Shanghai Key Laboratory of Psychotic Disorders Shanghai200030China Institute of Psychological and Behavioral Science Shanghai Jiao Tong UniversityShanghai200030China Shanghai Quanlan Technology Co. LtdShanghai201602China

An electroencephalographic(EEG)signature of auditory hallucinations(AHs)is important for facilitating the diagnosis and treatment of AHs in *** recorded EEG from 25 schizophrenia patients with recurrent *** the period of AHs,EEG recordings exhibited significantly elevated beta2-band power in the temporal region,as compared to those recorded in the absence of AHs or during stimulation with verbal *** further generated methamphetamine-treated rhesus monkeys exhibiting psychosis-like behaviors,including repetitive sudden searching actions in the absence of external intrusion,suggesting the occurrence of *** EEG beta2-band power in the temporal region of these monkeys was enhanced immediately after methamphetamine treatment and positively correlated with the frequency of sudden searching ***,the enhancement of temporal beta2-band oscillations represents a signature for AHs in both patients and a monkey model of psychosis,and this monkey model can be used for developing closed-loop neuromodulation approaches for the treatment of refractory AHs in schizophrenia.

关键词： Schizophrenia Auditory hallucinations Non-human primate Methamphetamine Electroencephalography

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Exploring the brain-like Properties of Deep Neural Networks:A Neural Encoding Perspective

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Machine intelligence Research 2022年第5期19卷 439-455页

作者： Qiongyi Zhou Changde Du Huiguang He Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesBeijing 100190China

Nowadays,deep neural networks(DNNs)have been equipped with powerful representation *** deep convolutional neural networks(CNNs)that draw inspiration from the visual processing mechanism of the primate early visual cortex have outperformed humans on object categorization and have been found to possess many brain-like ***,vision transformers(ViTs)have been striking paradigms of DNNs and have achieved remarkable improvements on many vision tasks compared to *** is natural to ask how the brain-like properties of ViTs *** the model paradigm,we are also interested in the effects of factors,such as model size,multimodality,and temporality,on the ability of networks to model the human visual pathway,especially when considering that existing research has been limited to *** this paper,we systematically evaluate the brain-like properties of 30 kinds of computer vision models varying from CNNs and ViTs to their hybrids from the perspective of explaining brain activities of the human visual cortex triggered by dynamic *** on two neural datasets demonstrate that neither CNN nor transformer is the optimal model paradigm for modelling the human visual *** reveal hierarchical correspondences to the visual pathway as CNNs ***,we find that multi-modal and temporal networks can better explain the neural activities of large parts of the visual cortex,whereas a larger model size is not a sufficient condition for bridging the gap between human vision and artificial *** study sheds light on the design principles for more brain-like *** code is available at https://***/QYiZhou/LWNeuralEncoding.

关键词： Convolutional neural network(CNN) vision transformer(Vi T) multi-modal networks spatial-temporal networks visual neural encoding brain-like neural networks

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Tuning in to Neural Encoding: Linking Human brain and Artificial Supervised Representations of Language 26

Tuning in to Neural Encoding: Linking Human Brain and Artifi...

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26th European Conference on Artificial intelligence, ECAI 2023

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

ISBN: (纸本)9781643684369

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. © 2023 The Authors.

关键词： Neural networks

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Multi-channel Neural Signal Recording System for an Implantable brain-Computer Interface

Multi-channel Neural Signal Recording System for an Implanta...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Qingya Li Zhiwei Zhang Mingxia Shi Xiaoyan Tao Institute of Automation Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Simultaneous recordings of neural activity at massive scope, in the long term, and under bio-safety conditions, could provide crucial information, which helps in better understanding the operation mechanism of the brain and promotes the clinical application evolution for the brain-computer interface. For this purpose, a multi-channel neural signal recording system is presented, which can record up to 2048-channel neural signals by multiple connections of a customized collection system. The system consists of a sensor array module, a central controller module, and an upper computer module. Using the modular design method, the sensor array module can be contrived by changing the number of channels. The single-channel data acquisition module has a sampling resolution of 16 bits, and a sampling rate of 30 KSamples/s. The central controller module can establish a connection between the sensor array module and the upper computer module, and control their operations. The upper computer module can display the data results. The system verifies the performance of the multi-channel data acquisition through the analog neural signal.

关键词： Protocols Array signal processing Data acquisition Neural activity Process control Rats Universal Serial Bus brain-computer interfaces Recording Sensor arrays

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Design and Construction of a Spherical Magnet Array for Multi-dimensional Tactile Perception

Design and Construction of a Spherical Magnet Array for Mult...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Gui, Mei-Jiang Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Feng, Zhen-Qiu Li, Hao Xiang, Tian-Yu Huang, De-Xing Yao, Bo-Xian Hou, Zeng-Guang Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Cas Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China Macau University of Science and Technology Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering 999078 China

ISBN: (纸本)9798350327182

Soft magnetic sensors provide considerable merits for tactile perception, such as high precision, simple wiring, and easy expansion to large sensing areas. However, current magnetic sensors basically rely on data-driven methods, difficult to establish the relationship for the two-stage conversion (i.e., from magnetic signals to spatial displacements, then to force signals). In this study, a spherical magnet array is designed with magnetic powder and hyper-elastic rubber for multi-dimensional tactile perception. Besides, a highly interpretable and calculable method is proposed to analyze the two-stage conversion according to magnetic dipole model and dynamic Young's modulus. A nine-spherical-magnet array is constructed, and extensive analysis in terms of different magnet numbers are conducted in COMSOL environments. The results demonstrate the proposed method's high accuracy with mean square errors of 0.02 mT and 9.90 mN for the two stages, respectively. This fact also indicates the great potential of our method to various applications of tactile perception. © 2023 IEEE.

关键词： Mean square error

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The interaction between KIF21A and KANK1 regulates dendritic morphology and synapse plasticity in neurons

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Neural Regeneration Research 2025年第1期20卷 209-223页

作者： Shi-Yan Sun Lingyun Nie Jing Zhang Xue Fang Hongmei Luo Chuanhai Fu Zhiyi Wei Ai-Hui Tang Hefei National Research Center for Physical Sciences at the Microscale CAS Key Laboratory of Brain Function and DiseaseMinistry of Education Key Laboratory for Membrane-less Organelles and Cellular DynamicsDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhui ProvinceChina Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefeiAnhui ProvinceChina CAS Center for Excellence in Molecular Cell Sciences Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhui ProvinceChina Department of Neurobiology School of Life SciencesSouthern University of Science and TechnologyShenzhenGuangdong ProvinceChina Brain Research Center Southern University of Science and TechnologyShenzhenGuangdong ProvinceChina

Morphological alterations in dendritic spines have been linked to changes in functional communication between neurons that affect learning and ***-4 KIF21A helps organize the microtubule-actin network at the cell cortex by interacting with KANK1;however,whether KIF21A modulates dendritic structure and function in neurons remains *** this study,we found that KIF21A was distributed in a subset of dendritic spines,and that these KIF21A-positive spines were larger and more structurally plastic than KIF21A-negative ***,the interaction between KIF21A and KANK1 was found to be critical for dendritic spine morphogenesis and synaptic *** of either KIF21A or KANK1 inhibited dendritic spine morphogenesis and dendritic branching,and these deficits were fully rescued by coexpressing full-length KIF21A or KANK1,but not by proteins with mutations disrupting direct binding between KIF21A and KANK1 or binding between KANK1 and *** down KIF21A in the hippocampus of rats inhibited the amplitudes of long-term potentiation induced by high-frequency stimulation and negatively impacted the animals’cognitive *** together,our findings demonstrate the function of KIF21A in modulating spine morphology and provide insight into its role in synaptic function.

关键词： actin cytoskeleton dendrite KANK1 KIF21A microtubule spine morphology spine synaptic plasticity talin1

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Accurate Perception of Manipulation Force Makes Vascular Intervention Safer

Accurate Perception of Manipulation Force Makes Vascular Int...

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2023 International Conference on Biomimetic intelligence and Robotics

作者： Yao, Bo-Xian Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Feng, Zhen-Qiu Gui, Mei-Jiang Hou, Zeng-Guang The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China

Manipulation force plays an important role in ensuring the safety of vascular intervention. Due to its extreme slightness, few studies have achieved accurate perception. In this study, a vision-based perception system is developed to obtain the manipulation force during guidewire delivery. In the system, an analysis framework is designed with the Hough transform and a fully connected neural network to establish the accurate transformation from visual image to haptic force. Experimental results indicate that the perception system achieves a minimal error (less than 0.1 N) in predicting the true force. It provides an effective solution for real-time monitoring dynamic manipulation in the context of vascular intervention. The proposed method also holds great potential for applications that require accurate force perception and control in various fields. © 2023 The Authors. Published by Elsevier B.V.

关键词： Hough transforms

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Manipulation skill learning on multi-step complex task based on explicit and implicit curriculum learning

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science China(Information sciences) 2022年第1期65卷 259-261页

作者： Naijun LIU Tao LU Yinghao CAI Rui WANG Shuo WANG Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Recently, remarkable progress of deep reinforcement learning(DRL) with regard to robot skill learning has been witnessed [1]. However, manipulation skill learning on multistep complicated tasks with DRL still poses great challenges owing to its higher exploration of state-action space and uninformative sparse rewards. To alleviate these problems,

关键词： Curricula

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Lateral interaction by Laplacian‐based graph smoothing for deep neural networks

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CAAI Transactions on intelligence technology 2023年第4期8卷 1590-1607页

作者： Jianhui Chen Zuoren Wang Cheng‐Lin Liu Institute of Neuroscience State Key Laboratory of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghaiChina State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina School of Future Technology University of Chinese Academy of SciencesBeijingChina School of Life Science and Technology ShanghaiTech UniversityShanghaiChina

Lateral interaction in the biological brain is a key mechanism that underlies higher cognitive *** self‐organising map(SOM)introduces lateral interaction in a general form in which signals of any modality can be *** approaches directly incorporate SOM learning rules into neural networks,but incur complex operations and poor *** efficient way to implement lateral interaction in deep neural networks is not well *** use of Laplacian Matrix‐based Smoothing(LS)regularisation is proposed for implementing lateral interaction in a concise *** authors’derivation and experiments show that lateral interaction implemented by SOM model is a special case of LS‐regulated k‐means,and they both show the topology‐preserving *** authors also verify that LS‐regularisation can be used in conjunction with the end‐to‐end training paradigm in deep auto‐***,the benefits of LS‐regularisation in relaxing the requirement of parameter initialisation in various models and improving the classification performance of prototype classifiers are ***,the topologically ordered structure introduced by LS‐regularisation in feature extractor can improve the generalisation performance on classification ***,LS‐regularisation is an effective and efficient way to implement lateral interaction and can be easily extended to different models.

关键词： artificial neural networks biologically plausible Laplacian‐based graph smoothing lateral interaction machine learning

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