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Correction to: Regional optimum frequency analysis of resting-state fMRI data for early detection of Alzheimer’s disease biomarkers

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Multimedia Tools and Applications 2022年第6期82卷 9551-9552页

作者： Garg, Gaurav Prasad, Girijesh Garg, Lalit Miyakoshi, Makoto Nakai, Toshiharu Coyle, Damien Embedded & Robotics BrainAlive Research Pvt. Ltd Kanpur India School of Computing and Intelligent Systems Magee Campus Ulster University Londonderry UK Faculty of Information & Communication Technology University of Malta Msida Malta Swartz Center for Computational Neuroscience Institute for Neural Computation University of California San Diego San Diego USA Neuroimaging & Informatics Lab (Niinf) National Centre for Geriatrics and Gerontology (NCGG) Obu Japan

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware

arXiv

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arXiv 2022年

作者： Müller-Cleve, Simon F. Fra, Vittorio Khacef, Lyes Pequeño-Zurro, Alejandro Klepatsch, Daniel Forno, Evelina Ivanovich, Diego G. Rastogi, Shavika Urgese, Gianvito Zenke, Friedemann Bartolozzi, Chiara Istituto Italiano di Tecnologia Event-driven Perception in Robotics Genoa Italy Politecnico di Torino EDA Group Torino Italy Bio-Inspired Circuits and Systems Lab Zernike Institute for Advanced Materials Groningen Cognitive Systems and Materials Center University of Groningen Groningen Netherlands Laboratory of Neural Computation Istituto Italiano di Tecnologia Genova Italy Silicon Austria Labs JKU LIT SAL eSPML Lab Austria Johannes Kepler University Linz JKU LIT SAL eSPML Lab Austria International Centre for Neuromorphic Systems Western Sydney University Australia Biocomputation Research Group University of Hertfordshire United Kingdom Friedrich Miescher Institute for Biomedical Research Neurobiology Basel Switzerland University of Basel Basel Switzerland

Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches have reached outstanding accuracies in such tasks, but their implementation on conventional embedded solutions is still very computationally and energy expensive. Tactile sensing in robotic applications is a representative example where real-time processing and energy efficiency are required. Following a brain-inspired computing approach, we propose a new benchmark for spatio-temporal tactile pattern recognition at the edge through Braille letter reading. We recorded a new Braille letters dataset based on the capacitive tactile sensors of the iCub robot's fingertip. We then investigated the importance of spatial and temporal information as well as the impact of event-based encoding on spike-based computation. Afterward, we trained and compared feedforward and recurrent Spiking neural Networks (SNNs) offline using Backpropagation Through Time (BPTT) with surrogate gradients, then we deployed them on the Intel Loihi neuromorphic chip for fast and efficient inference. We compared our approach to standard classifiers, in particular to the Long Short-Term Memory (LSTM) deployed on the embedded NVIDIA Jetson GPU, in terms of classification accuracy, power, and energy consumption together with computational delay. Our results show that the LSTM reaches ∼97% of accuracy, outperforming the recurrent SNN by ∼17 % when using continuous frame-based data instead of event-based inputs. However, the recurrent SNN on Loihi with event-based inputs is ∼500 times more energy-efficient than the LSTM on Jetson, requiring a total power of only ∼30 mW. This work proposes a new benchmark for tactile sensing and highlights the challenges and opportunities of event-based encoding, neuromorphic hardware, and spike-based computing for spatio-temporal pattern recognition at the edge. Copyright © 2022, The Authors. All rights reserved.

关键词： Long short-term memory

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A Hierarchical Emotion Regulated Sensorimotor Model:Case Studies

A Hierarchical Emotion Regulated Sensorimotor Model:Case Stu...

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第28届中国控制与决策会议

作者： Junpei Zhong Rony Novianto Mingjun Dai Xinzheng Zhang Angelo Cangelosi Centre for Robotics and Neural Systems Plymouth University Laboratory for Intelligent Dynamics and Representation Waseda University Centre of Quantum Computation and Intelligent Systems University of Technology Shenzhen Key Lab of Advanced Communication and Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen University School of Electrical and Information Engineering Jinan University

ISBN: (纸本)9781467397155

Inspired by the hierarchical cognitive architecture and the perception-action model(PAM)[14],we propose that the internal status acts as a kind of common-coding representation which affects,mediates and even regulates the sensorimotor *** regulation can be depicted in the Bayesian framework,that is why cognitive agents are able to generate behaviours with subtle differences according to their emotion or recognize the emotion by perception.A novel recurrent neural network called recurrent neural network with parametric bias units(RNNPB) runs in three modes,constructing a two-level emotion regulated learning model,was further applied to testify this theory in two different cases.

关键词： Embodied Emotion Modelling Cognitive Architecture Recurrent neural Networks Non-verbal Emotion Expression Social robotics

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Real-time classification of multi-modal sensory data for prosthetic hand control

Real-time classification of multi-modal sensory data for pro...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Iris Kyranou Agamemnon Krasoulis Mustafa Suphi Erden Kianoush Nazarpour Sethu Vijayakumar Edinburgh Centre of Robotics Heriot-Watt University UK Institute for Adaptive and Neural Computation and the Institute of Perception Action and Behaviour University of Edinburgh UK School of Engineering and Physical Sciences Heriot-Watt University UK School of Electrical and Electronic Engineering Newcastle University UK Institute of Perception Action and Behaviour University of Edinburgh UK

ISBN: (纸本)9781509032884

Recent work on myoelectric prosthetic control has shown that the incorporation of accelerometry information along with surface electromyography (sEMG) has the potential of improving the performance and robustness of a prosthetic device by increasing the classification accuracy. In this study, we investigated whether myoelectric control could further benefit from the use of additional sensory modalities such as gyroscopes and magnetometers. We trained a multi-class linear discriminant analysis (LDA) classifier to discriminate between six hand grip patterns and used predictions to control a robotic prosthetic hand in real-time. We recorded initial training data by using a total number of 12 sEMG sensors, each of which integrated a 9 degree-of-freedom inertial measurement unit (IMU). For classification, four different decoding schemes were used; 1) sEMG and IMU from all sensors 2) sEMG from all sensors, 3) IMU from all sensors and, finally, 4) sEMG and IMU from a nearly optimal subset of sensors. These schemes were evaluated based on offline classification accuracy on the training data, as well as with task-related metrics such as completion rates and times for a pick-and-place real-time experiment. We found that the classifier trained with all the sensory modalities and sensors (condition 1) attained the best decoding performance by achieving a 90.4% completion rate with an average completion time of 41.9 sec in real-time experiments. We also found that classifiers incorporating sEMG and IMU information outperformed on average the ones that only used sEMG signals, even when the amount of sensors used was less than half in the former case. These results suggest that using extra modalities along with sEMG might be more beneficial than including additional sEMG sensors.

关键词： Prosthetic hand Feature extraction Robot sensing systems Decoding Real-time systems Three-dimensional displays

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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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25th Annual computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Normalization for sparse encoding of odors by a wide-field interneuron (vol 332, pg 721, 2011)

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SCIENCE 2011年第6035期332卷 1263-1263页

作者： Papadopoulou, M. Division of Biology Computation and Neural Systems Program California Institute of Technology Pasadena CA 91125 USA. Broad Fellows Program in Brain Circuitry California Institute of Technology Pasadena CA 91125 USA. Centre for Computational Neuroscience and Robotics (CCNR) School of Informatics University of Sussex Falmer Brighton BN1 9QJ UK. Max Planck Institute for Brain Research 60528 Frankfurt Germany.

Sparse coding presents practical advantages for sensory representations and memory storage. In the insect olfactory system, the representation of general odors is dense in the antennal lobes but sparse in the mushroom bodies, only one synapse downstream. In locusts, this transformation relies on the oscillatory structure of antennal lobe output, feed-forward inhibitory circuits, intrinsic properties of mushroom body neurons, and connectivity between antennal lobe and mushroom bodies. Here we show the existence of a normalizing negative-feedback loop within the mushroom body to maintain sparse output over a wide range of input conditions. This loop consists of an identifiable “giant” nonspiking inhibitory interneuron with ubiquitous connectivity and graded release properties.

关键词： Action Potentials Animals Arthropod Antennae/cytology Arthropod Antennae/physiology Axons/physiology Dendrites/physiology Excitatory Postsynaptic Potentials Feedback, Sensory Feedback, Sensory Female gamma-Aminobutyric Acid/physiology gamma-Aminobutyric Acid/physiology Grasshoppers/cytology Grasshoppers/physiology Inhibitory Postsynaptic Potentials Interneurons/physiology Male Mushroom Bodies/cytology Mushroom Bodies/physiology neural Inhibition Odors Olfactory Pathways/physiology

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How to be lost: Principled priming and pruning with particles in score following

How to be lost: Principled priming and pruning with particle...

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International Computer Music Conference, ICMC 2007

作者： Fox, Charles Quinn, John Robotics Research Group Dept. Engineering Science University of Oxford United Kingdom Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh United Kingdom

Previous work in score following has provided methods for aligning a skilled live performance to a symbolic or audio score. In the Bayesian framework, ideal generative models require O(n) computations at each real time step where n is the length of the score. In practice, heuristic thresholds have been used to consider only a subspace of generative models with high priors conditioned on the previous state. These heuristics work well for skilled performances but fail when large errors are made by amateur musicians. We present a novel Priming Particle Filter for audio scores which places the order-limiting heuristic on a firm foundation and adds the ability to recover from large errors by using psychologically-inspired bottom-up priming in addition to regular sequential importance sampling.

关键词： Importance sampling

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Dual-coding representations for robot vision programming in Tekkotsu

Dual-coding representations for robot vision programming in ...

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作者： Touretzky, David S. Halelamien, Neil S. Tira-Thompson, Ethan J. Wales, Jordan J. Usui, Kei Computer Science Department Carnegie Mellon University Pittsburgh PA 15213 United States Computation and Neural Systems Program California Institute of Technology California United States Robotics Institute Carnegie Mellon University Pittsburgh PA 15213 United States P. O. Box 821650 Vancouver WA 98682 Canada

We describe complementary iconic and symbolic representations for parsing the visual world. The iconic pixmap representation is operated on by an extensible set of "visual routines" (Ullman, 1984;Forbus et al., 2001). A symbolic representation, in terms of lines, ellipses, blobs, etc., is extracted from the iconic encoding, manipulated algebraically, and re-rendered iconically. The two representations are therefore duals, and iconic operations can be freely intermixed with symbolic ones. The dual-coding approach offers robot programmers a versatile collection of primitives from which to construct application-specific vision software. We describe some sample applications implemented on the Sony AIBO. © Springer Science+Business Media, LLC 2007.

关键词： Computer vision

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