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Wearable Sensors: Large‐Scale Patterning of Reactive Surfaces for Wearable and Environmentally Deployable Sensors (Adv. Mater. 28/2020)

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Advanced Materials 2020年第28期32卷

作者： Giusy Matzeu Laia Mogas‐Soldevila Wenyi Li Arin Naidu Trent H. Turner Roger Gu Patricia R. Blumeris Patrick Song Daniel G. Pascal Giulia Guidetti Meng Li Fiorenzo G. Omenetto Silklab Department of Biomedical Engineering Tufts University Medford MA 02155 USA Center for Applied Brain and Cognitive Science Tufts University Medford MA 02155 USA Department of Electrical and Computer Engineering Tufts University Medford MA 02155 USA Department of Physics Tufts University Medford MA 02155 USA Laboratory for Living Devices Tufts University Medford MA 02155 USA

On the role of theory and modeling in neuroscience

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arXiv 2020年

作者： Levenstein, Daniel Alvarez, Veronica A. Amarasingham, Asohan Azab, Habiba Chen, Zhe S. Gerkin, Richard C. Hasenstaub, Andrea Iyer, Ramakrishnan Jolivet, Renaud B. Marzen, Sarah Monaco, Joseph D. Prinz, Astrid A. Quraishi, Salma Santamaria, Fidel Shivkumar, Sabyasachi Singh, Matthew F. Traub, Roger Rotstein, Horacio G. Nadim, Farzan Redish, A. David Montreal Neurological Institute McGill University MontrealQC Canada Laboratory on Neurobiology of Compulsive Behaviors National Institute on Alcohol Abuse and Alcoholism NIH BethesdaMD United States Department of Neuroscience Center for Magnetic Resonance Research University of Minnesota MinneapolisMN United States School of Life Sciences Arizona State University TempeAZ United States Department of Otolaryngology - Head and Neck Surgery University of California San Francisco San FranciscoCA United States Allen Institute for Brain Science SeattleWA United States Maastricht University Maastricht Netherlands Department of Biomedical Engineering Johns Hopkins University School of Medicine BaltimoreMD United States Department of Biology University of Texas at San Antonio San AntonioTX United States Department of Psychological & Brain Sciences Department of Electrical & Systems Engineering Washington University in St. Louis St. LouisMO United States IBM T.J. Watson Research Center AI Foundations Yorktown HeightsNY United States W. M. Keck Science Department Pitzer Scripps and Claremont McKenna Colleges ClaremontCA United States Department of Biology Emory University AtlantaGA United States Department of Mathematics City College of New York United States Departments of Biology Computer Science and Psychology The Graduate Center City University of New York United States Brain and Cognitive Sciences University of Rochester RochesterNY United States Federated Department of Biological Sciences New Jersey Institute of Technology Rutgers University Institute for Brain and Neuroscience Research New Jersey Institute of Technology NewarkNJ United States Department of Neuroscience University of Minnesota MinneapolisMN United States Department of Psychiatry Neuroscience & Physiology New York University School of Medicine United States Mila - Quebec AI Institute MontrealQC Canada

In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative models and theories each play a distinct role in defining and bridging levels of abstraction will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment. © 2020, CC BY.

关键词： Computation theory

Machine learning in acoustics: Theory and applications

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arXiv 2019年

作者： Bianco, Michael J. Gerstoft, Peter Traer, James Ozanich, Emma Roch, Marie A Gannot, Sharon Deledalle, Charles-Alban Li, Weichang Scripps Institution of Oceanography University of California San Diego La JollaCA92037 United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Department of Computer Science San Diego State University San DiegoCA92182 United States Faculty of Engineering Bar-Ilan University Ramat-Gan5290002 Israel Department of Electrical and Computer Engineering University of California San Diego La JollaCA92037 United States Aramco Research Center-Houston Aramco Services Company HoustonTX77084 United States

Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of statistical techniques for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in five acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, seismic exploration, and environmental sounds in everyday scenes. Copyright © 2019, The Authors. All rights reserved.

关键词： Acoustic fields

Slip detection with combined tactile and visual information

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arXiv 2018年

作者： Li, Jianhua Dong, Siyuan Adelson, Edward MIT CambridgeMA02139 United States Department of Electrical Engineering and Computer Science CSAIL MIT CambridgeMA02139 United States Department of Brain and Cognitive Sciences CSAIL MIT CambridgeMA02139 United States

Slip detection plays a vital role in robotic manipulation and it has long been a challenging problem in the robotic community. In this paper, we propose a new method based on deep neural network (DNN) to detect slip. The training data is acquired by a GelSight tactile sensor and a camera mounted on a gripper when we use a robot arm to grasp and lift 94 daily objects with different grasping forces and grasping positions. The DNN is trained to classify whether a slip occurred or not. To evaluate the performance of the DNN, we test 10 unseen objects in 152 grasps. A detection accuracy as high as 88.03% is achieved. It is anticipated that the accuracy can be further improved with a larger dataset. This method is beneficial for robots to make stable grasps, which can be widely applied to automatic force control, grasping strategy selection and fine manipulation. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

Flexible neural representation for physics prediction 18

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Flexible neural representation for physics prediction

Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Damian Mrowca Chengxu Zhuang Elias Wang Nick Haber Li Fei-Fei Joshua B. Tenenbaum Daniel L. K. Yamins Department of Computer Science Stanford CA Department of Psychology Stanford CA Department of Electrical Engineering Stanford CA Department of Psychology Stanford CA and Department of Pediatrics Stanford CA and Department of Biomedical Data Sciencem Stanford CA Department of Brain and Cognitive Sciences MIT Cambridge MA and Department of Computer Science and Artificial Intelligence Laboratory MIT Cambridge MA Department of Computer Science Stanford CA and Department of Psychology Stanford CA and Wu Tsai Neurosciences Institutem Stanford CA

Humans have a remarkable capacity to understand the physical dynamics of objects in their environment, flexibly capturing complex structures and interactions at multiple levels of detail. Inspired by this ability, we propose a hierarchical particle-based object representation that covers a wide variety of types of three-dimensional objects, including both arbitrary rigid geometrical shapes and deformable materials. We then describe the Hierarchical Relation Network (HRN), an end-to-end differentiable neural network based on hierarchical graph convolution, that learns to predict physical dynamics in this representation. Compared to other neural network baselines, the HRN accurately handles complex collisions and nonrigid deformations, generating plausible dynamics predictions at long time scales in novel settings, and scaling to large scene configurations. These results demonstrate an architecture with the potential to form the basis of next-generation physics predictors for use in computer vision, robotics, and quantitative cognitive science.

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FV 12. Non-invasive Suppression of Essential Tremor via Phase-Locked Disruption of its Temporal Coherence

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Clinical Neurophysiology 2021年第8期132卷 e35-e36页

作者： S. Schreglmann D. Wang R. Peach J. Li X. Zhang A. Latorre E. Rhodes E. Panella E.S. Boyden M. Barahona S. Santaniello J. Rothwell K. Bhatia N. Grossman Uniklinikum Würzburg Klinik und Poliklinik Neurologie Würzburg Germany University College London Institute of Neurology London United Kingdom Massachussetts Institute of Technology Computer Science and Artificial Intelligence Laboratory Cambridge MA United States Imperial College London Centre for Mathematics of Precision Healthcare Department of Mathematics London United Kingdom Imperial College London Division of Brain Sciences Department of Medicine London United Kingdom University of Connecticut Biomedical Engineering Department and CT Institute for the Brain and Cognitive Sciences Storrs CT United States Imperial College London Department of Physics London United Kingdom Massachussetts Institute of Technology Media Lab Cambridge MA United States Massachussetts Institute of Technology McGovern Institute for Brain Research Cambridge MA United States Harvard University and MIT Broad Institute Cambridge MA United States Massachussetts Institute of Technology Department of Biological Engineering Cambridge MA United States Massachussetts Institute of Technology Department of Brain and Cognitive Sciences Cambridge MA United States Massachussetts Institute of Technology Center for Neurobiological Engineering Cambridge MA United States Imperial College London UK Dementia Research Institute London United Kingdom Imperial College London Centre for Bio-Inspired Technology Department of Electrical and Electronic Engineering London United Kingdom Imperial College London Centre for Neurotechnology London United Kingdom

Aberrant neural oscillations hallmark the pathophysiology of numerous neurological and psychiatric disorders. Here, we first report a method to accurately track the phase of neural oscillations in real-time by a Hilbert transform that avoids the characteristic Gibbs distortion at the end of the signal, aka endpoint-corrected Hilbert transform (ecHT). The ecHT method maintains the same computational complexity class of the original Hilbert transform allowing implementation in simple digital hardware. We then used the ecHT method to show that the aberrant neural oscillation that hallmarks treatment-resistant essential tremor (ET), the most common adult movement disorder, can be noninvasively supressed via transcranial electrical stimulation at a fixed phase lag over the cerebellar hemisphere ipsilateral to the tremor movement. This was tested in a quadruple-replication randomized way including stimulation at 6 fixed phases, sham and without phase-locking. In a total of 11 subjects, the suppression of ET activity was sustained after the end of the stimulation and was phenomenologically predicted, post-hoc, from the features of the tremor movement before the start of the stimulation. To test for reproducibility, 6 of the original participants werer stimulated exactly the same way three years after the original experiments: the observed, significant stimulation response remained, i.e. responders continued to respond and non-responders did not. Finally, we used a highly-comparative feature extraction (> 8000 features) with statistical learning and neurophysiological computational modelling to show that the suppression of ET activity can be mechanistically attributed to a disruption of the temporal coherence in the tremor movement that can be originated in a higher bursting entropy at the cortico-olivo-cerebello-thalamic circuitry. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may represent a powerful neuromodul

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Effects of Grammatical Gender on Object Description 39

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Effects of Grammatical Gender on Object Description

39th Annual Meeting of the cognitive science Society: Computational Foundations of Cognition, CogSci 2017

作者： Semenuks, Arturs Philips, Webb Dalca, Ioana Kim, Cora Boroditsky, Lera Department of Cognitive Science University of California San Diego United States School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Brain Language Laboratory Department of Philosophy and Humanities Freie Universität Berlin Germany

ISBN: (纸本)9780991196760

Can grammatical gender influence how people conceptualize the referents of nouns? Using an implicit measure, we investigated whether such an effect could be found in a task where neither grammatical nor biological gender is highlighted. In the current study, conducted in English, speakers of French, German and Romanian with knowledge of English were asked to generate adjectives they associate with referents of nouns. Afterwards, the gender valence of the adjectives was measured. The results showed that participants generated more feminine adjectives for nouns with majority feminine translations compared to nouns with majority masculine translations. We found a stronger effect of grammatical gender for some semantic categories than for others. Significant effects of grammatical gender were present starting with the 2nd adjective generated by participants (effects were stronger for adjectives generated 2nd and 3rd by participants, as opposed to the 1st adjective). © CogSci 2017.

关键词： Semantics

Explaining the unique nature of individual gait patterns with deep learning

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arXiv 2018年

作者： Horst, Fabian Lapuschkin, Sebastian Samek, Wojciech Müller, Klaus-Robert Schöllhorn, Wolfgang I. Department of Training and Movement Science Institute of Sport Science Johannes Gutenberg-University Mainz Mainz Rhineland-Palatinate Germany Department of Video Coding & Analytics Fraunhofer Heinrich Hertz Institute Berlin Germany Department of Electrical Engineering & Computer Science Technical University Berlin Berlin Germany Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of Max Planck Institute for Informatics Saarbrücken Saarland Germany

Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems. However, in most cases this comes with the disadvantage of acting as a black box, rarely providing information about what made them arrive at a particular prediction. This black box aspect of ML techniques can be problematic especially in medical diagnoses, so far hampering a clinical acceptance. The present paper studies the uniqueness of individual gait patterns in clinical biomechanics using DNNs. By attributing portions of the model predictions back to the input variables (ground reaction forces and full-body joint angles), the Layer-Wise Relevance Propagation (LRP) technique reliably demonstrates which variables at what time windows of the gait cycle are most relevant for the characterisation of gait patterns from a certain individual. By measuring the time-resolved contribution of each input variable to the prediction of ML techniques such as DNNs, our method describes the first general framework that enables to understand and interpret non-linear ML methods in (biomechanical) gait analysis and thereby supplies a powerful tool for analysis, diagnosis and treatment of human gait. Copyright © 2018, The Authors. All rights reserved.

关键词： Gait analysis

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

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arXiv 2019年

作者： Lapuschkin, Sebastian Wäldchen, Stephan Binder, Alexander Montavon, Grégoire Samek, Wojciech Müller, Klaus-Robert Department of Video Coding & Analytics Fraunhofer Heinrich Hertz Institute Einsteinufer 37 Berlin10587 Germany Department of Electrical Engineering and Computer Science Technische Universität Berlin Marchstr. 23 Berlin10587 Germany ISTD Pillar Singapore University of Technology and Design 8 Somapah Rd Singapore487372 Singapore Department of Brain and Cognitive Engineering Korea University Anam-dong Seongbuk-ku Seoul136-713 Korea Republic of Max Planck Institut für Informatik Campus E1 4 Stuhlsatzenhausweg Saarbrücken66123 Germany

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning