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An integrated transcriptomic cell atlas of human neural organoids (vol 635, pg 690, 2024)

NATURE 2025年第8045期637卷 E21-E21页

作者： He, Zhisong Dony, Leander Fleck, Jonas Simon Szalata, Artur Li, Katelyn X. Sliskovic, Irena Lin, Hsiu-Chuan Santel, Malgorzata Atamian, Alexander Quadrato, Giorgia Sun, Jieran Pasca, Sergiu P. Camp, J. Gray Theis, Fabian J. Treutlein, Barbara Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Institute of Computational Biology Computational Health Center Helmholtz Munich Neuherberg Germany Department Genes and Environment Max Planck Institute of Psychiatry Munich Germany International Max Planck Research School for Translational Psychiatry (IMPRS-TP) Munich Germany TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Physiology and Pharmacology Karolinska Institutet Stockholm Sweden Institute of Human Biology (IHB) Roche Pharma Research and Early Development Roche Innovation Center Basel Basel Switzerland Biozentrum University of Basel Basel Switzerland Department of Stem Cell Biology and Regenerative Medicine Keck School of Medicine University of Southern California Los Angeles CA USA Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC Keck School of Medicine University of Southern California Los Angeles CA USA Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA Stanford Brain Organogenesis Program Wu Tsai Neurosciences Institute and Bio-X Stanford CA USA Neural Stem Cell Institute Albany NY USA UK Dementia Research Institute at the University of Edinburgh Edinburgh Bioquarter Edinburgh UK Centre for Discovery Brain Sciences School of Biomedical Sciences College of Medicine and Veterinary Medicine The University of Edinburgh Edinburgh UK Human Technopole Milan Italy Department of Oncology and Hemato-Oncology University of Milan Milan Italy

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Viewpoints: Approaches to defining and investigating fear

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NATURE NEUROSCIENCE 2019年第8期22卷 1205-1216页

作者： Mobbs, Dean Adolphs, Ralph Fanselow, Michael S. Barrett, Lisa Feldman LeDoux, Joseph E. Ressler, Kerry Tye, Kay M. Department of Humanities and Social Sciences and Computation and Neural Systems Program California Institute of Technology Pasadena California USA Departments of Psychology and Psychiatry and Biobehavioral Sciences University of California Los Angeles Los Angeles California USA Department of Psychology Northeastern University Boston Massachusetts USA Martinos Center for Biomedical Imaging and Department of Psychiatry Massachusetts General Hospital and Harvard Medical School Boston Massachusetts USA Center for Neural Science New York University New York New York USA Nathan Kline Institute New York State Office of Mental Health New York New York USA Departments of Psychiatry and Child and Adolescent Psychiatry NYU Langone Medical School New York New York USA Division of Depression & Anxiety Disorders McLean Hospital Belmont Massachusetts USA Department of Psychiatry at Harvard Medical School Boston Massachusetts USA Salk Institute for Biological Studies La Jolla California USA

There is disagreement on how best to define and investigate fear. Nature Neuroscience asked Dean Mobbs to lead experts from the fields of human and animal affective neuroscience to discuss their viewpoints on how to d... 详细信息

关键词： Amygdala Psychology

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One Tap at a Time: Correlating Sensorimotor Synchronization with Brain Signatures of Temporal Processing

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Cerebral cortex communications 2020年第1期1卷 tgaa036页

作者： Gina M D'Andrea-Penna John R Iversen Andrea A Chiba Alexander K Khalil Victor H Minces Neurosciences Graduate Program UC San Diego La Jolla CA 92093 USA. Institute for Neural Computation UC San Diego La Jolla CA 92093 USA. Department of Cognitive Science UC San Diego La Jolla CA 92093 USA.

The ability to integrate our perceptions across sensory modalities and across time, to execute and coordinate movements, and to adapt to a changing environment rests on temporal processing. Timing is essential for basic daily tasks, such as walking, social interaction, speech and language comprehension, and attention. Impaired temporal processing may contribute to various disorders, from attention-deficit hyperactivity disorder and schizophrenia to Parkinson's disease and dementia. The foundational importance of timing ability has yet to be fully understood; and popular tasks used to investigate behavioral timing ability, such as sensorimotor synchronization (SMS), engage a variety of processes in addition to the neural processing of time. The present study utilizes SMS in conjunction with a separate passive listening task that manipulates temporal expectancy while recording electroencephalographic data. Participants display a larger N1-P2 evoked potential complex to unexpected beats relative to temporally predictable beats, a differential we call the timing response index (TRI). The TRI correlates with performance on the SMS task: better synchronizers show a larger brain response to unexpected beats. The TRI, derived from the perceptually driven N1-P2 complex, disentangles the perceptual and motor components inherent in SMS and thus may serve as a neural marker of a more general temporal processing.

关键词： electroencephalography (EEG) rhythm sensorimotor synchronization (SMS) sensory integration timing

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A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems 15

A Neuro-Inspired Approach to Solve a Simultaneous Location a...

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15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018

作者： Menezes, Matheus Chaves De Freitas, Edison Pignaton Cheng, Sen De Oliveira, Alexandre Cesar Muniz De Almeida Ribeiro, Paulo Rogerio Graduate Program on Computer Science Federal University of Maranhão Sao Luis Brazil Computational Neuroscience Ruhr University Bochum Institute for Neural Computation Bochum Germany Department of Informatics Federal University of Maranhão Sao Luis Maranhao Brazil Department of Computer Engineering Federal University of Maranhão Sao Luis Maranhao Brazil

ISBN: (纸本)9781538695821

This paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. This proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. The work here reported extends this approach by merging partial information acquired by multiple robots. The results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. Thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots. © 2018 IEEE.

关键词： Mapping

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Self-Awareness for Autonomous Systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems computational modeling

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Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques

arXiv

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arXiv 2018年

作者： Celotto, Marco de Luca, Chiara Muratore, Paolo Resta, Francesco Mascaro, Anna Letizia Allegra Pavone, Francesco Saverio de Bonis, Giulia Paolucci, Pier Stanislao Department of Physics "Sapienza" University of Rome Rome00185 Italy LENS University of Florence Florence50019 Italy Istituto di Neuroscienze CNR Pisa56124 Italy Department of Physics University of Florence Florence50019 Italy INFN Rome00185 Italy IIT - Neural Computation Lab CNCS@UniTn Rovereto38068 Italy PhD Program in Behavioural Neuroscience "Sapienza" University of Rome Rome00185 Italy PhD Program in Cognitive Neuroscience SISSA Trieste34136 Italy

Slow waves (SWs) are spatio-temporal patterns of cortical activity that occur both during natural sleep and anesthesia and are preserved across species. Even though electrophysiological recordings have been largely used to characterize brain states, they are limited in the spatial resolution and cannot target specific neuronal population. Recently, large-scale optical imaging techniques coupled with functional indicators overcame these restrictions, and new pipelines of analysis and novel approaches of SWs modelling are needed to extract relevant features of the spatio-temporal dynamics of SWs from these highly spatially resolved data-sets. Here we combined wide-field fluorescence microscopy and a transgenic mouse model expressing a calcium indicator (GCaMP6f) in excitatory neurons to study SW propagation over the meso-scale under ketamine anesthesia. We developed a versatile analysis pipeline to identify and quantify the spatio-temporal propagation of the SWs. Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neuronal activity, the response of fluorescence proteins and the slow waves dynamics. The simulator was capable of synthesizing artificial signals that could reliably reproduce several features of the SWs observed in vivo, thus enabling a calibration tool for the analysis pipeline. Comparison of experimental and simulated data shows the robustness of the analysis tools and its potential to uncover mechanistic insights of the Slow Wave Activity (SWA). Copyright © 2018, The Authors. All rights reserved.

关键词： Fluorescence microscopy

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BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains

arXiv

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arXiv 2018年

作者： Jiang, Linxing Stocco, Andrea Losey, Darby M. Abernethy, Justin A. Prat, Chantel S. Rao, Rajesh P.N. University of Washington Paul G. Allen School of Computer Science & Engineering SeattleWA98195 United States University of Washington Department of Psychology SeattleWA98195 United States University of Washington Institute for Learning and Brain Sciences SeattleWA98195 United States Carnegie Mellon University Joint Program in Neural Computation & Machine Learning PittsburghPA15213 United States Carnegie Mellon University Center for the Neural Basis of Cognition PittsburghPA15213 United States University of Washington Institute for Neuroengineering SeattleWA98195 United States University of Washington Center for Neurotechnology SeattleWA98195 United States

We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as "Senders" whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the "Receiver," who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects’ decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the collaborative task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainN

关键词： Electrophysiology

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Feedforward architectures driven by inhibitory interactions

arXiv

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arXiv 2017年

作者： Billeh, Yazan N. Schaub, Michael T. Computation and Neural Systems Program California Institute of Technology Université Catholique de Louvain Massachusetts Institute of Technology Institute for Data Systems and Society Massachusetts Institute of Technology Department of Engineering Science University of Oxford

Directed information transmission is paramount for many social, physical, and biological sys- tems. For neural systems, scientists have studied this problem under the paradigm of feedforward networks for decades. In most models of feedforward networks, activity is exclusively driven by excitatory neurons and the wiring patterns between them, while inhibitory neu- rons play only a stabilizing role for the network dy- namics. Motivated by recent experimental discoveries of hippocampal circuitry, cortical circuitry, and the di- versity of inhibitory neurons throughout the brain, here we illustrate that one can construct such networks even if the connectivity between the excitatory units in the system remains random. This is achieved by endowing inhibitory nodes with a more active role in the network. Our findings demonstrate that apparent feedforward activity can be caused by a much broader network- architectural basis than often assumed. Copyright © 2017, The Authors. All rights reserved.

关键词： Brain

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei

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Scientific data 2018年第1期5卷 180063页

作者： Wolfgang M Pauli Amanda N Nili J Michael Tyszka Division of Humanities and Social Sciences California Institute of Technology Pasadena CA 91125 USA. Computation and Neural Systems Program California Institute of Technology Pasadena CA 91125 USA. Northwestern University Evanston IL 60208 USA.

Recent advances in magnetic resonance imaging methods, including data acquisition, pre-processing and analysis, have benefited research on the contributions of subcortical brain nuclei to human cognition and behavior. At the same time, these developments have led to an increasing need for a high-resolution probabilistic in vivo anatomical atlas of subcortical nuclei. In order to address this need, we constructed high spatial resolution, three-dimensional templates, using high-accuracy diffeomorphic registration of T- and T- weighted structural images from 168 typical adults between 22 and 35 years old. In these templates, many tissue boundaries are clearly visible, which would otherwise be impossible to delineate in data from individual studies. The resulting delineations of subcortical nuclei complement current histology-based atlases. We further created a companion library of software tools for atlas development, to offer an open and evolving resource for the creation of a crowd-sourced in vivo probabilistic anatomical atlas of the human brain.

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