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38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016

作者： Tseng, Christine Carstensen, Alexandra Regier, Terry Xu, Yang Computation and Neural Systems California Institute of Technology PasadenaCA91125 United States Department of Psychology University of California BerkeleyCA94720 United States Department of Linguistics Cognitive Science Program University of California BerkeleyCA94720 United States

ISBN: (纸本)9780991196739

Investigations of the Sapir-Whorf hypothesis often ask whether there is a difference in the non-linguistic behavior of speakers of two languages, generally without modeling the underlying process. Such an approach leaves underexplored the relative contributions of language and universal aspects of cognition, and how those contributions differ across languages. We explore the naming and non-linguistic pile-sorting of spatial scenes across speakers of five languages via a computational model grounded in an influential proposal: that language will affect cognition when non-linguistic information is uncertain. We report two findings. First, native language plays a small but significant role in predicting spatial similarity judgments across languages, consistent with earlier findings. Second, the size of the native-language role varies systematically, such that finer-grained semantic systems appear to shape similarity judgments more than coarser-grained systems do. These findings capture the tradeoff between language-specific and universal forces in cognition, and how that tradeoff varies across languages. © 2016 Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016. All rights reserved.

关键词： Semantics

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Differential covariance: A new method to estimate functional connectivity in fMRI

arXiv

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arXiv 2017年

作者： Lin, Tiger W. Krishnan, Giri P. Bazhenov, Maxim Sejnowski, Terrence J. Howard Hughes Medical Institute Computational Neurobiology Laboratory Salk Institute for Biological Studies San DiegoCA92037 United States Neurosciences Graduate Program University of California San Diego San DiegoCA92092 United States Institute for Neural Computation University of California San Diego San DiegoCA92092 Department of Medicine University of California San Diego San DiegoCA92092 United States

Measuring functional connectivity from fMRI is important in understanding processing in cortical networks. However, because brain's connection pattern is complex, currently used methods are prone to produce false connections. We introduce here a new method that uses derivative for estimating functional connectivity. Using simulations, we benchmarked our method with other commonly used methods. Our method achieves better results in complex network simulations. This new method provides an alternative way to estimate functional connectivity. Copyright © 2017, The Authors. All rights reserved.

关键词： Complex networks

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Differential covariance: A new class of methods to estimate sparse connectivity from neural recordings

arXiv

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arXiv 2017年

作者： Lin, Tiger W. Das, Anup Krishnan, Giri P. Bazhenov, Maxim Sejnowski, Terrence J. Howard Hughes Medical Institute Computational Neurobiology Laboratory Salk Institute for Biological Studies San DiegoCA92037 United States Neurosciences Graduate Program University of California San Diego San DiegoCA92092 United States Institute for Neural Computation University of California San Diego San DiegoCA92092 Department of Medicine University of California San Diego San DiegoCA92092 United States Jacobs School of Engineering University of California San Diego San DiegoCA92092 United States

With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship between multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson et al., 2008), they produce spurious connections. The general linear model (GLM), which models spikes trains as Poisson processes (Okatan et al., 2005;Truccolo et al., 2005;Pillow et al., 2008), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential (LFP) recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved better or similar performance to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals. Copyright © 2017, The Authors. All rights reserved.

关键词： Electrophysiology

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Author Correction: 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 2024年第10期21卷 1959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

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Self-sustained activity in balanced networks with low firing-rate

arXiv

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arXiv 2018年

作者： Borges, F.S. Protachevicz, P.R. Pena, R.F.O. Lameu, E.L. Matias, F.S. Higa, G.S.V. Kihara, A.H. Antonopoulos, C.G. Iarosz, K.C. de Pasquale, R. Roque, A.C. Batista, A.M. Ji, P. Center for Mathematics Computation and Cognition Federal University of ABC São Bernardo do Campo SP Brazil Institute of Physics University of São Paulo São Paulo SP Brazil Graduate in Science Program - Physics State University of Ponta Grossa PR Brazil Laboratory of Neural Systems Department of Physics University of São Paulo Ribeirão Preto SP Brazil National Institute for Space Research São José dos Campos SP Brazil Department of Physics Humboldt University Berlin Germany Cognitive Neuroimaging Unit CEA DRF/I2BM INSERM Université Paris-Sud Université Paris-Saclay Gif/YvetteF-91191 France Institute of Physics Federal University of Alagoas Maceió AL Brazil Department of Mathematical Sciences University of Essex Wivenhoe Park United Kingdom Potsdam Institute for Climate Impact Research Potsdam Germany Department of Physiology and Biophysics ICB University of São Paulo São Paulo SP Brazil Department of Mathematics and Statistics State University of Ponta Grossa Ponta Grossa PR Brazil Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China

The brain can display self-sustained activity (SSA), which is persistent firing of neurons in the absence of external stimuli. This spontaneous activity shows low neuronal firing rates and is observed in diverse in vitro and in vivo situations. In this work, we study the influence of excitatory/inhibitory balance, connection density, and network size on the self-sustained activity of a neuronal network model. We build a random network of adaptive exponential integrate-and-fire (AdEx) neuron models connected through inhibitory and excitatory chemical synapses. The AdEx model mimics several behaviours of biological neurons, such as spike initiation, adaptation, and bursting patterns. In an excitation/inhibition balanced state, if the mean connection degree (K) is fixed, the firing rate does not depend on the network size (N), whereas for fixed N, the firing rate decreases when K increases. However, for large K, SSA states can appear only for large N. We show the existence of SSA states with similar behaviours to those observed in experimental recordings, such as very low and irregular neuronal firing rates, and spike-train power spectra with slow fluctuations, only for balanced networks of large size. Copyright © 2018, The Authors. All rights reserved.

关键词： neural networks

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A bidirectional brain-machine interface connecting alert rodents to a dynamical system 37

A bidirectional brain-machine interface connecting alert rod...

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37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015

作者： Boi, Fabio Semprini, Marianna Mussa Ivaldi, Ferdinando A. Panzeri, Stefano Vato, Alessandro Neural Computation Laboratory Istituto Italiano di Tecnologia Rovereto Italy Physical Medicine and Rehabilitation Mechanical and Biomedical Engineering Northwestern University Sensory Motor Performance Program Rehabilitation Institute of Chicago ChicagoIL United States

ISBN: (纸本)9781424492718

We present a novel experimental framework that implements a bidirectional brain-machine interface inspired by the operation of the spinal cord in vertebrates that generates a control policy in the form of a force field. The proposed experimental set-up allows connecting the brain of freely moving rats to an external device. We tested this apparatus in a preliminary experiment with an alert rat that used the interface for acquiring a food reward. The goal of this approach to bidirectional interfaces is to explore the role of voluntary neural commands in controlling a dynamical system represented by a small cart moving on vertical plane and connected to a water/pellet dispenser. © 2015 IEEE.

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Inter Subject Correlation of Brain Activity during Visuo-Motor Sequence learning

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8834卷 35-41页

作者： Miyapuram, Krishna Prasad Pamnani, Ujjval Doya, Kenji Bapi, Raju S. Cognitive Science Program Indian Institute of Technology Gandhinagar India Neural Computation Unit Okinawa Institute of Science and Technology Okinawa Japan Center for Neural & Cognitive Sciences Department of Computer and Information Sciences University of Hyderabad India

Brain imaging using functional MRI allows us to understand brain function while participants are engaged in meaningful tasks. Traditionally the experimental paradigms have been limited to repeated presentation of stimuli to participants followed by a model-based analysis of the data. The Inter Subject Correlation (ISC) analysis allows a model-free analysis while participants are presented with naturalistic stimuli such as watching a movie. We extend the ISC approach to a learning paradigm in which participants are repeatedly performing a motor sequence in response to visual stimuli. We qualitatively compare the correlation results across learning sessions. The preliminary result we observe is shift of correlation activity in cerebellum across sessions. A modelbased analysis identifying task related activity compared to baseline is also reported. © Springer International Publishing Switzerland 2014.

关键词： Magnetic resonance imaging

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Bidirectional neural Interface: Closed-Loop Feedback Control for Hybrid neural Systems

Bidirectional Neural Interface: Closed-Loop Feedback Control...

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

作者： Zane Chou Jeffrey Lim Sophie Brown Melissa Keller Joseph Bugbee Frédéric D. Broccard Massoud L. Khraiche Gabriel A. Silva Gert Cauwenberghs the undergraduate Bioengineering program at the University of California San Diego CA 92092 USA the Institute for Neural Computation and the Department of Bioengineering at the University of California San Diego CA 92093 USA the Department of Bioengineering and Ophthalmology at the University of California San Diego CA 92093 USA

ISBN: (纸本)9781424492695

Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.

关键词： Bidirectional neural Interface Closed-Loop Feedback Control Hybrid neural Systems Closed-loop feedback control hybrid systems Closed loop systems neural Prostheses bidirection communication Biological Substance Abuse Detection Induction loop systems network parameters Bidirectional

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Synchronization properties of heterogeneous neuronal networks with mixed excitability type

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Physical Review E 2015年第3期91卷 032813-032813页

作者： Michael J. Leone Brandon N. Schurter Benjamin Letson Victoria Booth Michal Zochowski Christian G. Fink Mathematics Department New College of Florida Sarasota Florida 34243 USA Program in Neural Computation Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Physics Department Berea College Berea Kentucky 40404 USA Mathematics Department Ohio Wesleyan University Delaware Ohio 43015 USA Mathematics Department University of Pittsburgh Pittsburgh Pennsylvania 15260 USA Mathematics Department and Anesthesiology Department University of Michigan Ann Arbor Michigan 48109 USA Physics Department and Biophysics Program University of Michigan Ann Arbor Michigan 48109 USA Physics Department and Neuroscience Program Ohio Wesleyan University Delaware Ohio 43015 USA

We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily “hijack” neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.

关键词： neural networks (Computer science) SYNCHRONIZATION HETEROGENEOUS computing PERTURBATION (Mathematics) CORRELATION (Statistics)

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26th Annual computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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