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arXiv 2020年

作者： Chen, Zhi Chen, Lu Zhou, Xiang Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Eng. SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

Dialogue state tracking (DST) is a crucial module in dialogue management. It is usually cast as a supervised training problem, which is not convenient for on-line optimization. In this paper, a novel companion teaching based deep reinforcement learning (DRL) framework for on-line DST optimization is proposed. To the best of our knowledge, this is the first effort to optimize the DST module within DRL framework for on-line task-oriented spoken dialogue systems. In addition, dialogue policy can be further jointly updated. Experiments show that on-line DST optimization can effectively improve the dialogue manager performance while keeping the flexibility of using predefined policy. Joint training of both DST and policy can further improve the performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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A consensus protocol for functional connectivity analysis in the rat brain (vol 26, pg 673, 2023)

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NATURE NEUROSCIENCE 2023年第6期26卷 1127-1128页

作者： Grandjean, Joanes Desrosiers-Gregoire, Gabriel Anckaerts, Cynthia Angeles-Valdez, Diego Ayad, Fadi Barriere, David A. Blockx, Ines Bortel, Aleksandra Broadwater, Margaret Cardoso, Beatriz M. Celestine, Marina Chavez-Negrete, Jorge E. Choi, Sangcheon Christiaen, Emma Clavijo, Perrin Colon-Perez, Luis Cramer, Samuel Daniele, Tolomeo Dempsey, Elaine Diao, Yujian Doelemeyer, Arno Dopfel, David Dvorakova, Lenka Falfan-Melgoza, Claudia Fernandes, Francisca F. Fowler, Caitlin F. Fuentes-Ibanez, Antonio Garin, Clement M. Gelderman, Eveline Golden, Carla E. M. Guo, Chao C. G. Henckens, Marloes J. A. G. Hennessy, Lauren A. Herman, Peter Hofwijks, Nita Horien, Corey Ionescu, Tudor M. Jones, Jolyon Kaesser, Johannes Kim, Eugene Lambers, Henriette Lazari, Alberto Lee, Sung-Ho Lillywhite, Amanda Liu, Yikang Liu, Yanyan Y. Lopez-Castro, Alejandra Lopez-Gil, Xavier Ma, Zilu MacNicol, Eilidh Madularu, Dan Mandino, Francesca Marciano, Sabina McAuslan, Matthew J. McCunn, Patrick McIntosh, Alison Meng, Xianzong Meyer-Baese, Lisa Missault, Stephan Moro, Federico Naessens, Daphne M. P. Nava-Gomez, Laura J. Nonaka, Hiroi Ortiz, Juan J. Paasonen, Jaakko Peeters, Lore M. Pereira, Mickael Perez, Pablo D. Pompilus, Marjory Prior, Malcolm Rakhmatullin, Rustam Reimann, Henning M. Reinwald, Jonathan Del Rio, Rodrigo Triana Rivera-Olvera, Alejandro Ruiz-Perez, Daniel Russo, Gabriele Rutten, Tobias J. Ryoke, Rie Sack, Markus Salvan, Piergiorgio Sanganahalli, Basavaraju G. Schroeter, Aileen Seewoo, Bhedita J. Selingue, Erwan Seuwen, Aline Shi, Bowen Sirmpilatze, Nikoloz Smith, Joanna A. B. Smith, Corrie Sobczak, Filip Stenroos, Petteri J. Straathof, Milou Strobelt, Sandra Sumiyoshi, Akira Takahashi, Kengo Torres-Garcia, Maria E. Tudela, Raul van den Berg, Monica van der Marel, Kajo van Hout, Aran T. B. Vertullo, Roberta Vidal, Benjamin Vrooman, Roel M. Wang, Victora X. Wank, Isabel Watson, David J. G. Yin, Ting Zhang, Yongzhi Zurbruegg, Stefan Achard, Sophie Alcauter, Sarael Auer, Dorothee P. Barbier, Emmanuel L. Baudewig, Juergen Beckmann, Chri Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada Integrated Program in Neuroscience McGill University Montreal QC Canada Bio-imaging Lab University of Antwerp Antwerp Belgium µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico Biological and Biomedical Engineering McGill University Montreal QC Canada McConnell Brain Imaging Centre McGill University Montreal QC Canada Montreal Neurological Institute McGill University Montreal QC Canada UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France Department of Neurology and Neurosurgery McGill University Montreal QC Canada Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tuebingen Germany Graduate Training Centre of Neuroscience International Max

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

关键词： Databases Neural circuits

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Publisher Correction: An implantable piezoelectric ultrasound stimulator (ImPULS) for deep brain activation

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Nature communications 2024年第1期15卷 6280页

作者： Jason F Hou Md Osman Goni Nayeem Kian A Caplan Evan A Ruesch Albit Caban-Murillo Ernesto Criado-Hidalgo Sarah B Ornellas Brandon Williams Ayeilla A Pearce Huseyin E Dagdeviren Michelle Surets John A White Mikhail G Shapiro Fan Wang Steve Ramirez Canan Dagdeviren Media Lab Massachusetts Institute of Technology Cambridge MA 02139 USA. Department of Brain and Cognitive Sciences McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge MA 02139 USA. Department of Psychological and Brain Sciences The Center for Systems Neuroscience Boston University Boston 02215 MA USA. Division of Chemistry and Chemical Engineering California Institute of Technology Pasadena CA 91125 USA. Center for Systems Neuroscience Neurophotonics Center Department of Biomedical Engineering Boston University 610 Commonwealth Ave. Boston MA 02215 USA. Department of Neurosurgery Faculty of Medicine Istanbul University Istanbul 34093 Turkey. Media Lab Massachusetts Institute of Technology Cambridge MA 02139 USA. canand@media.mit.edu.

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Mechanics of Soft-Body Rolling Motion without External Torque

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Physical Review Letters 2025年第19期134卷 198401-198401页

作者： Xudong Liang Yimiao Ding Zihao Yuan Yuxuan Han Yutang Zhou Junqi Jiang Zongling Xie Peng Fei Yixuan Sun Pan Jia Guoying Gu Zheng Zhong Feifei Chen Guangwei Si Zhefeng Gong School of Science Harbin Institute of Technology Shenzhen 518055 China Department of Neurology of the Fourth Hospital and School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou 310058 China Robotics Institute and State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Cognitive Science and Mental Health Institute of Biophysics Chinese Academy of Sciences 15 Datun Road Beijing 100101 China University of Chinese Academy of Sciences 19A Yuquan Road Beijing 100049 China Huazhong University of Science and Technology Optical and Electronic Information Wuhan 430074 China Zhejiang Lab Hangzhou 311121 China Shanghai Key Laboratory of Intelligent Robotics Meta Robotics Institute Shanghai Jiao Tong University Shanghai 200240 China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine Integration State Key Laboratory of Brain-machine Intelligence Zhejiang University 1369 West Wenyi Road Hangzhou 311121 China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Hangzhou 310058 China

The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we construct a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.

关键词： Creatinine

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Optogenetics: Tools for Controlling Brain Cells with Light

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Molecular Frontiers Journal 2019年第2期3卷 129-137页

作者： Edward S. Boyden, III McGovern Institute Media Lab Center for Neurobiological Engineering Department of Brain and Cognitive Sciences Department of Biological Engineering and Koch Institute Massachusetts Institute of Technology Cambridge MA USA

The brain is made out of an incredible diversity of cells called neurons, which have different shapes, are made of different molecules, and that change in different ways in diseases. In optogenetics, microbial opsins, natural proteins that convert light into electrical signals, are genetically expressed in neurons. Then, light pulses can be used to turn neurons on, revealing how they trigger behaviors, disease states, or therapeutic effects, or to turn neurons off, revealing what functions or dysfunctions they are necessary for.

关键词： Neuron Brain Opsin Optogenetics Neurotechnology Neuroengineering Neuroscience Bioelectricity Neural Circuits

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Supplemental educational program to heighten the impact of research - an opportunity for OA imaging

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Osteoarthritis imaging 2023年第2期3卷 100155页

作者： Deborah Burstein Rudolph Mitchell Emery Brown Martha L Gray Associate Professor Department of Radiology Beth Israel Deaconess Medical Center Harvard Medical School 330 Brookline Avenue Boston MA USA. Associate Director for Research and Evaluation in the Teaching + Learning Lab Massachusetts Institute of Technology Cambridge MA USA. Edward Hood Taplin Professor of Computational Neuroscience and Health Sciences and Technology Department of Brain and Cognitive Sciences and Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA. J.W. Kieckefer Professor of Medical and Electrical Engineering Department of Electrical Engineering and Computer Science and Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA.

Objective:To develop and evaluate a supplementary educational program ("IMPACT") centered on enabling participants to consider specifically and articulate explicitly the best path for and potential impact of their research. Design:Participants (trainees) and faculty mentors were from all areas of biomedical research. The group worked longitudinally in small, rotating groups, through a process of developing a written statement ("Impact Statement"), an overview ("Impact Storyline") and an oral presentation ("Impact Case") of their work. Results:One hundred and eighty-seven Fellows enrolled in the program. Of the 179 (96%) Fellows who completed the program, 159 (89%) responded to a post-program survey; 94% indicated that IMPACT was a significant learning experience, 89% indicated that they were more able to identify the long-term potential of their research, 95% felt more able to talk about their work to diverse audiences. Conclusion:This voluntary educational program was appreciated by the participants and led to increased confidence in their ability to drive their science towards a clear impact and communicating that potential to others. This type of program may aid in redirecting some of the efforts and resources of imaging in OA from the large focus on technical developments to more direct biological and clinical questions which might be resolved with current technology.

关键词： Communication Diversity Impact Research

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NAS-Count: Counting-by-Density with Neural Architecture Search 16th

NAS-Count: Counting-by-Density with Neural Architecture Sear...

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16th European Conference on Computer Vision, ECCV 2020

作者： Hu, Yutao Jiang, Xiaolong Liu, Xuhui Zhang, Baochang Han, Jungong Cao, Xianbin Doermann, David School of Electronic and Information Engineering Beihang University Beijing China Key Laboratory of Advanced Technologies for Near Space Information Systems Ministry of Industry and Information Technology Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beijing China YouKu Cognitive and Intelligent Lab Alibaba Group Hangzhou China Beihang University Beijing China Computer Science Department Aberystwyth University AberystwythSY23 3FL United Kingdom Department of Computer Science and Engineering University at Buffalo New York United States

ISBN: (纸本)9783030585419

Most of the recent advances in crowd counting have evolved from hand-designed density estimation networks, where multi-scale features are leveraged to address the scale variation problem, but at the expense of demanding design efforts. In this work, we automate the design of counting models with Neural Architecture Search (NAS) and introduce an end-to-end searched encoder-decoder architecture, Automatic Multi-Scale Network (AMSNet). Specifically, we utilize a counting-specific two-level search space. The encoder and decoder in AMSNet are composed of different cells discovered from micro-level search, while the multi-path architecture is explored through macro-level search. To solve the pixel-level isolation issue in MSE loss, AMSNet is optimized with an auto-searched Scale Pyramid Pooling Loss (SPPLoss) that supervises the multi-scale structural information. Extensive experiments on four datasets show AMSNet produces state-of-the-art results that outperform hand-designed models, fully demonstrating the efficacy of NAS-Count. © 2020, Springer Nature Switzerland AG.

关键词： Decoding

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Mechanics of soft-body rolling motion without external torque

arXiv

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arXiv 2024年

作者： Liang, Xudong Ding, Yimiao Yuan, Zihao Jiang, Junqi Xie, Zongling Fei, Peng Sun, Yixuan Gu, Guoying Zhong, Zheng Chen, Feifei Si, Guangwei Gong, Zhefeng School of Science Harbin Institute of Technology Shenzhen518055 China Department of Neurology The Fourth Hospital and School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou310058 China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-Machine Integration State Key Laboratory of Brain-Machine Intelligence Zhejiang University 1369 West Wenyi Road Hangzhou311121 China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Hangzhou310058 China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai200240 China Robotics Institute School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Brain and Cognitive Science Institute of Biophysics Chinese Academy of Sciences 15 Datun Road Beijing100101 China University of Chinese Academy of Sciences 19A Yuquan Road Beijing100049 China Huazhong University of Science and Technology Optical and Electronic Information Wuhan430074 China Zhejiang Lab. Hangzhou311121 China

The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we constructed a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics. © 2024, CC BY.

关键词： Pneumatics

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A scalable chatbot platform leveraging online community posts: A proof-of-concept study

arXiv

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arXiv 2020年

作者： Jo, Sihyeon Yang, Seung Hee Yoo, Seungryong Zuo, Tong Im, Sangwon Kim, Hee-Eun Han, SangWook Kim, Seong-Woo Electrical & Computer Eng Seoul National University Interdisciplinary Program in Cognitive Science Seoul National University Nuclear Engineering Seoul National University Electronics & Information Eng North China Univ. of Technology Aerospace Engineering Seoul National University Curatorial Team Art Center Nabi Nabi Lab Art Center Nabi Korea Republic of Graduate School of Engineering Practice Seoul National University

The development of natural language processing algorithms and the explosive growth of conversational data are encouraging researches on the human-computer conversation. Still, getting qualified conversational data on a large scale is difficult and expensive. In this paper, we verify the feasibility of constructing a data-driven chatbot with processed online community posts by using them as pseudo-conversational data. We argue that chatbots for various purposes can be built extensively through the pipeline exploiting the common structure of community posts. Our experiment demonstrates that chatbots created along the pipeline can yield the proper responses. Copyright © 2020, The Authors. All rights reserved.

关键词： Pipelines

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Dynamic humanoid locomotion over uneven terrain with streamlined perception-control pipeline

arXiv

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arXiv 2020年

作者： Lee, Moonyoung Kwon, Youngsun Lee, Sebin Choe, JongHun Park, Junyong Jeong, Hyobin Heo, Yujin Kim, Min-Su Sungho, Jo Yoon, Sung-Eui Oh, Jun-Ho Humanoid Robot Research Center Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Scalable Graphics Vision and Robotics Lab School of Computing Korea Advanced Institute of Science and Technology Korea Republic of Neuro-Machine Augmented Intelligence Laboratory School of Computing Korea Advanced Institute of Science and Technology Korea Republic of Korea Republic of

Although bipedal locomotion provides the ability to traverse unstructured environments, it requires careful planning and control to safely walk across without falling. This poses an integrated challenge for the robot to perceive, plan, and control its movements, especially with dynamic motions where the robot may have to adapt its swing-leg trajectory on-the-fly in order to safely place its foot on the uneven terrain. In this paper we present an efficient geometric footstep planner and the corresponding walking controller that enables a humanoid robot to dynamically walk across uneven terrain at speeds up to 0.3 m/s. As dynamic locomotion, we refer first to the continuous walking motion without stopping, and second to the on-the-fly replanning of the landing footstep position in middle of the swing phase during the robot gait cycle. This is mainly achieved through the streamlined integration between an efficient sampling-based planner and robust walking controller. The footstep planner is able to generate feasible footsteps within 5 milliseconds, and the controller is able to generate a new corresponding swing leg trajectory as well as the whole-body motion to dynamically balance the robot to the newly updated footsteps. The proposed perception-control pipeline is evaluated and demonstrated with real experiments using a full-scale humanoid to traverse across uneven terrains featured by static stepping stones, dynamically movable stepping stone, or narrow path. © 2020, CC BY.

关键词： Controllers

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