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Exercised blood plasma promotes hippocampal neurogenesis in the Alzheimer's disease rat brain

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Journal of Sport and Health science 2024年第2期13卷 245-255页

作者： Cecilie Skarstad Norevik Aleksi M.Huuha Ragnhild N.RФsbjФrgen Linda Hildegard Bergersen Kamilla Jacobsen Rodrigo Miguel-dos-Santos Liv Ryan Belma Skender Jose Bianco N.Moreira Asgeir Kobro-Flatmoen Menno P.Witter Nathan Scrimgeour Atefe R.Tari Cardiac Exercise research Group(CERG) Department of Circulation and Medical ImagingNorwegian University of Science and Technology7491TrondheimNorway Department of Neurology and Clinical Neurophysiology St.Olavs University Hospital7030TrondheimNorway institute of Oral Biology University of Oslo0372OsloNorway Department of Physiology Federal University of SergipeS~ao Cristov~ao49100-000SergipeBrazil centre of Molecular Inflammation research Department of Clinical and Molecular MedicineNorwegian University of Science and Technology7491TrondheimNorway Kavli institute for Systems Neuroscience Centre for Neural Computationand Egil and Pauline Braathen and Fred Kavli Centre for Cortical MicrocircuitsNorwegian University of Science and Technology7030TrondheimNorway K.G.Jebsen centre for Alzheimer’s Disease Norwegian University of Science and Technology7030TrondheimNorway

Background:Exercise training promotes brain plasticity and is associated with protection against cognitive impairment and Alzheimer’s disease(AD).These beneficial effects may be partly mediated by blood-borne factors.Here we used an in vitro model of AD to investigate effects of blood plasma from exercise-trained donors on neuronal viability,and an in vivo rat model of AD to test whether such plasma impacts cognitive function,amyloid pathology,and neurogenesis.Methods:Mouse hippocampal neuronal cells were exposed to AD-like stress using amyloid-βand treated with plasma collected from human male donors 3 h after a single bout of high-intensity exercise.For in vivo studies,blood was collected from exercise-trained young male Wistar rats(high-intensity intervals 5 days/week for 6 weeks).Transgenic AD rats(McGill-R-Thyl-APP)were inj ected 5 times/fortnight for 6 weeks at2 months or 5 months of age with either(a)plasma from the exercise-trained rats,(b)plasma from sedentary rats,or(c)saline.Cognitive function,amyloid plaque pathology,and neurogenesis were assessed.The plasma used for the treatment was analyzed for 23 cytokines.Results:Plasma from exercised donors enhanced cell viability by 44.1%(p=0.032)and reduced atrophy by 50.0%(pbrain.This effect may be partly due to reduced pro-inflammatory signaling molecules in exercised plasma.

关键词： Cytokines High-intensity interval training Inflammation Neurons Plasma transfusion

Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

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CAAI Transactions on intelligence technology 2024年第2期9卷 319-332页

作者： Xiaoqing Zhang Xiao Wu Zunjie Xiao Lingxi Hu Zhongxi Qiu Qingyang Sun Risa Higashita Jiang Liu research institute of Trustworthy Autonomous Systems and Department of Computer science and Engineering Southern University of Science and TechnologyShenzhenChina Tomey Corporation NagoyaJapan Guangdong Provincial Key Laboratory of brain‐inspired Intelligent computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhenChina Singapore Eye research institute SingaporeSingapore

Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

关键词： artificial intelligence deep learning deep neural networks image analysis image classification medical applications medical image processing

Solid-state non-volatile memories based on vdW heterostructure-based vertical-transport ferroelectric field-effect transistors

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science China(Information sciences) 2024年第6期67卷 198-206页

作者： Qiyu YANG Zheng-Dong LUO Fei XIAO Junpeng ZHANG Dawei ZHANG Dongxin TAN Xuetao GAN Yan LIU Zhufei CHU Yinshui XIA Genquan HAN Hangzhou institute of technology Xidian University State Key Discipline Laboratory of Wide Band Gap Semiconductor technology School of MicroelectronicsXidian University School of Artificial intelligence Xidian University School of Materials science and Engineering University of New South Wales (UNSW) Sydney ARC centre of Excellence in Future Low-Energy Electronics Technologies (FLEET) University of New South Wales (UNSW) Sydney Key Laboratory of Light Field Manipulation and Information Acquisition Ministry of Industry and Information Technologyand Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical University Faculty of Electrical Engineering and Computer science Ningbo University

Driven by the explosive development of data-centric computation applications, it is becoming urgent to develop in-memory computing devices that are beyond the von Neumann architecture with an arrangement of separated logic and memory components. The transistor-type solid-state non-volatile memories,such as ferroelectric field-effect transistors(Fe FETs), have long been regarded as a competitive candidate for future in-memory computing architectures. However, the density scaling towards high-density arrays would require advanced Fe FETs with reduced footprints, which remains a great challenge so far. Here, a vertical-transport(VT) Fe FET that flips the charge transport channel perpendicular to the substrate plane is proposed, in which a ferroelectric gate and a van der Waals(vd W) heterojunction channel are vertically integrated, effectively reducing the device footprints. The proposed VT-Fe FET shows not only the robust binary non-volatile memory states but also several key synaptic functionalities at the device level. An artificial neural network with supervised learning was simulated based on the device conductance switching properties,showing excellent classification accuracy for the MNIST handwritten digits. These findings suggest that the proposed VT-Fe FET could offer a new solution for future non-volatile memories as well as more advanced neuromorphic systems.

关键词： vdW heterostructure non-volatile memory vertical-transport transistor ferroelectric field-effect transistor memristive devices

众脑之力:通过开放神经影像数据和大规模协作催化神经精神疾病的创新发现

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science Bulletin 2024年第10期69卷 1536-1555页

作者：鲁彬陈骁 Francisco Xavier Castellanos Paul M.Thompson 左西年臧玉峰严超赣 CAS Key Laboratory of Behavioral science Institute of PsychologyBeijing 100101China Department of Psychology University of Chinese Academy of SciencesBeijing 100101China Department of Child and Adolescent Psychiatry NYU Grossman School of MedicineNew York 10016USA d Nathan Kline institute for Psychiatric research Orangeburg 10962USA Imaging Genetics Center Mark&Mary Stevens Institute for Neuroimaging&InformaticsKeck School of MedicineUniversity of Southern CaliforniaLos Angeles 90033USA Developmental Population Neuroscience research Center IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijing 100875China National Basic science Data Center Beijing 100190China centre for Cognition and brain Disorders The Affiliated Hospital of Hangzhou Normal UniversityHangzhou 310004China institute of Psychological science Hangzhou Normal UniversityHangzhou 310030China Zhejiang Key Laboratory for research in Assessment of Cognitive Impairment Hangzhou 311121China International Big-Data Center for Depression research Institute of PsychologyChinese Academy of SciencesBeijing 100101China Magnetic Resonance Imaging research Center Institute of PsychologyChinese Academy of SciencesBeijing 100101China

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders.By pooling images from various cohorts,statistical power has increased,enabling the detection of subtle abnormalities and robust associations,and fostering new research methods.Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment.Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies.We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders.However,challenges such as data harmonization across different sites,privacy protection,and effective data sharing must be addressed.With proper governance and open science practices,we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis,treatment selection,and outcome prediction,contributing to optimal brain health.

关键词： Magnetic resonance imaging Neuropsychiatric disorders Big data Open science Artificial intelligence

An Overview and Perspectives On Bidirectional intelligence: Lmser Duality, Double IA Harmony,and Causal computation

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 865-893页

作者： Lei Xu IEEE the Center for Cognitive Madmnes and computational Health(CMaCH) School of Electronic Information and Electrical EngineeringShanghai Jiao Tong University Neural Computation Research Centre in Brain and Intelligence Science-Technology Institute Shanghai ZhangJiang National Lab

Advances on bidirectional intelligence are overviewed along three threads,with extensions and new perspectives.The first thread is about bidirectional learning architecture,exploring five dualities that enable Lmser six cognitive functions and provide new perspectives on which a lot of extensions and particularlly flexible Lmser are proposed.Interestingly,either or two of these dualities actually takes an important role in recent models such as U-net,ResNet,and Dense Net.The second thread is about bidirectional learning principles unified by best yIng-yAng(IA)harmony in BYY system.After getting insights on deep bidirectional learning from a bird-viewing on existing typical learning principles from one or both of the inward and outward directions,maximum likelihood,variational principle,and several other learning principles are summarised as exemplars of the BYY learning,with new perspectives on advanced topics.The third thread further proceeds to deep bidirectional intelligence,driven by long term dynamics(LTD)for parameter learning and short term dynamics(STD)for image thinking and rational thinking in harmony.Image thinking deals with information flow of continuously valued arrays and especially image sequence,as if thinking was displayed in the real world,exemplified by the flow from inward encoding/cognition to outward reconstruction/transformation performed in Lmser learning and BYY learning.In contrast,rational thinking handles symbolic strings or discretely valued vectors,performing uncertainty reasoning and problem solving.In particular,a general thesis is proposed for bidirectional intelligence,featured by BYY intelligence potential theory(BYY-IPT)and nine essential dualities in architecture,fundamentals,and implementation,respectively.Then,problems of combinatorial solving and uncertainty reasoning are investigated from this BYY IPT perspective.First,variants and extensions are suggested for AlphaGoZero like searching tasks,such as traveling salesman problem(TSP)and attributed graph matching(AGM)that ar

关键词： Autoencoder LMSER duality outward attention associative recall concept formation imagining pattern transformation STD vs LTD RPCL skip connection feedback variational least redundancy Bayesian Ying Yang IA system best harmony best matching image thinking rational thinking intelligence potential theory Alpha-TSP Alpha-AGM graph matching ME Player BYY Follower constraint satisfaction causal potential theory

Denoised Internal Models:A brain-inspired Autoencoder Against Adversarial Attacks

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Machine intelligence research 2022年第5期19卷 456-471页

作者： Kai-Yuan Liu Xing-Yu Li Yu-Rui Lai Hang Su Jia-Chen Wang Chun-Xu Guo Hong Xie Ji-Song Guan Yi Zhou School of Life sciences and technology ShanghaiTech UniversityShanghai 201210China Shanghai Center for brain science and brain-inspired technology Shanghai 201602China School of Life sciences Tsinghua UniversityBeijing 100084China National Engineering Laboratory for brain-inspired intelligence technology and Application School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China centre for Artificial-intelligence Nanophotonics School of Optical-electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Despite its great success,deep learning severely suffers from robustness;i.e.,deep neural networks are very vulnerable to adversarial attacks,even the simplest ones.Inspired by recent advances in brain science,we propose the denoised internal models(DIM),a novel generative autoencoder-based model to tackle this challenge.Simulating the pipeline in the human brain for visual signal processing,DIM adopts a two-stage approach.In the first stage,DIM uses a denoiser to reduce the noise and the dimensions of inputs,reflecting the information pre-processing in the thalamus.Inspired by the sparse coding of memory-related traces in the primary visual cortex,the second stage produces a set of internal models,one for each category.We evaluate DIM over 42 adversarial attacks,showing that DIM effectively defenses against all the attacks and outperforms the SOTA on the overall robustness on the MNIST(Modified National institute of Standards and technology)dataset.

关键词： brain-inspired learning autoencoder robustness adversarial attack generative model

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 equivariances.However, 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

The Superior Colliculus:Cell Types,Connectivity,and Behavior

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Neuroscience Bulletin 2022年第12期38卷 1519-1540页

作者： Xue Liu Hongren Huang Terrance PSnutch Peng Cao Liping Wang Feng Wang Shenzhen Key Lab of Neuropsychiatric Modulation Guangdong Provincial Key Laboratory of Brain Connectome and BehaviorCAS Key Laboratory of Brain Connectome and Manipulationthe Brain Cognition and Brain Disease InstituteShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research InstitutionsShenzhen 518055China University of Chinese Academy of sciences Beijing 100049China Michael Smith Laboratories and Djavad Mowafaghian centre for brain Health University of British ColumbiaVancouver V6T 1Z4Canada National institute of Biological sciences Beijing 100049China

The superior colliculus(SC),one of the most well-characterized midbrain sensorimotor structures where visual,auditory,and somatosensory information are integrated to initiate motor commands,is highly conserved across vertebrate evolution.Moreover,cell-type-specific SC neurons integrate afferent signals within local networks to generate defined output related to innate and cognitive behaviors.This review focuses on the recent progress in understanding of phenotypic diversity amongst SC neurons and their intrinsic circuits and long-projection targets.We further describe relevant neural circuits and specific cell types in relation to behavioral outputs and cognitive functions.The systematic delineation of SC organization,cell types,and neural connections is further put into context across species as these depend upon laminar architecture.Moreover,we focus on SC neural circuitry involving saccadic eye movement,and cognitive and innate behaviors.Overall,the review provides insight into SC functioning and represents a basis for further understanding of the pathology associated with SC dysfunction.

关键词： Superior colliculus Glutamatergic neurons GABAergic neurons Neuronal circuits Innate behaviors

Learning deep IA bidirectional intelligence

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Frontiers of Information technology & Electronic Engineering 2020年第4期21卷 558-562页

作者： Lei XU centre for Cognitive Machines and computational Health(CMaCH) School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China neural computation research centre Brain and Intelligence Sci-Tech InstituteZhangjiang LabShanghai 201210China

There has been a framework sketched for learning deep bidirectional intelligence.The framework has an inbound that features two actions:one is the acquiring action,which gets inputs in appropriate patterns,and the other is A-S cognition,derived from the abbreviated form of words abstraction and self-organization,which abstracts input patterns into concepts that are labeled and understood by self-organizing parts involved in the concept into structural hierarchies.The top inner domain accommodates relations and a priori knowledge with the help of the A-I thinking action that is responsible for the accumulation-amalgamation and induction-inspiration.The framework also has an outbound that comes with two actions.One is called I-S reasoning,which makes inference and synthesis(I-S)and is responsible for performing various tasks including image thinking and problem solving,and the other is called the interacting action,which controls,communicates with,and inspects the environment.Based on this framework,we further discuss the possibilities of design intelligence through synthesis reasoning.

关键词： Abstraction Least mean square error reconstruction(Lmser) Cognition Image thinking Abstract thinking Synthesis reasoning