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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 1

BMC Neurosci 2017年第SUPPL 1期18卷 58-58页

作者： Sue Denham Panayiota Poirazi Erik De Schutter Karl Friston Ho Ka Chan Thomas Nowotny Dongqi Han Sungho Hong Sophie Rosay Tanja Wernle Alessandro Treves Sarah Goethals Romain Brette Tomas Van Pottelbergh Rodolphe Sepulchre Alex D. Bird Hermann Cuntz Pedro J. Gonçalves Jan-Matthis Lueckmann Giacomo Bassetto Marcel Nonnenmacher Jakob H. Macke Audrey J. Sederberg Jason N. MacLean Stephanie E. Palmer Ulisse Ferrari Christophe Gardella Olivier Marre Thierry Mora Emina Ibrahimovic Martin Müller Jean-Pascal Pfister Tushar Chauhan Timothée Masquelier Alexandre Montlibert Benoit R. Cottereau Moritz Helias Jannis Schuecker David Dahmen Sven Goedeke Alexandre Hyafil Ainhoa Hermoso-Mendizabal Pavel E. Rueda-Orozco Santiago Jaramillo David Robbe Jaime de la Rocha Marie Rooy Fani Koukouli David DiGregorio Uwe Maskos Boris Gutkin Andrey Yu Verisokin Darya V. Verveyko Dmitry E. Postnov Willy Wong Omid Talakoub Robert Chen Milos Popovic Dmitriy Lisitsyn Eric Drebitz Iris Grothe Sunita Mandon Andreas Kreiter Udo Ernst Peter A. Robinson Xuelong Zhao Kevin M. Aquino John D. Griffiths Grishma Mehta-Pandejee Natasha Gabay James MacLaurin Somwrita Sarkar Tim Kunze Jens Haueisen Thomas R. Knösche Subutai Ahmad Yuwei Cui Marcus Lewis Jeff Hawkins Simona Olmi Spase Petkoski Fabrice Bartolomei Maxime Guye Viktor Jirsa Hazem Toutounji Daniel Durstewitz Matteo Cantarelli Adrian Quintana Boris Marin Matt Earnshaw Padraig Gleeson Robert Court Robert McDougal R. Angus Silver Salvador Dura-Bernal Stephen Larson William W. Lytton Giovanni Idili Lorenzo Posani Simona Cocco Karel Ježek Rémi Monasson School of Psychology Faculty of Health & Human Sciences Plymouth University Plymouth Devon PL4 8AA UK Institute of Molecular Biology and Biotechnology (IMBB) Foundation for Research and Technology-Hellas (FORTH) Heraklion Crete Greece Okinawa Institute of Science and Technology Graduate University Okinawa Japan Wellcome Trust Centre for Neuroimaging Institute of Neurology UCL London WC1N 3BG UK School of Engineering and Informatics University of Sussex Brighton BN1 9QJ UK Graduate School Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Computational Neuroscience Unit Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Cognitive Neuroscience Area Scuola Internazionale Superiore di Studi Avanzati Trieste 34136 Italy Kavli Institute for Systems Neuroscience NTNU Trondheim 7030 Norway Institut de la Vision Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Paris 75012 France Department of Engineering University of Cambridge Cambridge Cambridgeshire CB2 1PZ UK Ernst Strüngmann Institute for Neuroscience Max Planck Society Frankfurt Germany Frankfurt Institute for Advanced Studies Frankfurt Germany Neural Systems Analysis Research Center Caesar An Associate of the Max Planck Society Bonn Germany Max Planck Institute for Biological Cybernetics Tübingen Germany Bernstein Center for Computational Neuroscience Tübingen Germany Present Address: Biomedical Engineering Department Georgia Institute of Technology Atlanta GA 30306 USA Department of Neurobiology University of Chicago Chicago IL 60637 USA Department of Organismal Biology and Anatomy University of Chicago Chicago IL 60637 USA Committee on Computational Neuroscience University of Chicago Chicago IL 60637 USA Institut de la Vision Sorbonne Universités INSERM Paris France Laboratoire de Physique Statistique Ecole Normale Supérieure Paris France Institute of Molecular Life Sciences University of Zürich 8057 Zürich Switzerland Institute of Neuroinformatics Univer

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brain-computer interface for smart vehicle: Detection of braking intention during simulated driving

Brain-computer interface for smart vehicle: Detection of bra...

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2014 International Winter Workshop on brain-computer Interface, BCI 2014

作者： Kim, Jeong-Woo Kim, Il-Hwa Haufe, Stefan Lee, Seong-Whan Dept. of Brain and Cognitive Engineering Korea Univ. Seoul Korea Republic of Dept. of Computer Science Berlin Institute of Technology Berlin Germany

ISBN: (纸本)9781479925889

Neuro-driving simulation framework was proposed in this article for studying neural correlates of braking intention in diversified driving situations. In addition, the possibility was investigated that these neural correlates can be used to detect a participant's braking intention prior to the behavioral response. Electroencephalographic (EEG) and electromyographic (EMG) signals were measured from fifteen participants during they were exposed to several kinds of traffic situations in a neuro-driving simulation framework. After that, the novel characteristic feature was extracted from the measured signals to categorize according to whether the driver intended to brake or not. This proposed novel feature consists of readiness potential (RP), event-related desynchronization (ERD) and event-related potential (ERP) as used in a previous study. The prediction performance of braking intention based on the proposed feature combination exhibited superior prediction performance than simple ERP feature used in a previous study. © 2014 IEEE.

关键词： brain computer interface

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Boosted deep neural networks and multi-resolution cochleagram features for voice activity detection 15

Boosted deep neural networks and multi-resolution cochleagra...

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15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014

作者： Zhang, Xiao-Lei Wang, DeLiang Department of Electronic Engineering Tsinghua University TNList Beijing China Department of Computer Science and Engineering and Center for Cognitive and Brain Sciences Ohio State University ColumbusOH United States

Voice activity detection (VAD) is an important frontend of many speech processing systems. In this paper, we describe a new VAD algorithm based on boosted deep neural networks (bDNNs). The proposed algorithm first generates multiple base predictions for a single frame from only one DNN and then aggregates the base predictions for a better prediction of the frame. Moreover, we employ a new acoustic feature, multi-resolution cochleagram (MRCG), that concatenates the cochleagram features at multiple spectrotemporal resolutions and shows superior speech separation results over many acoustic features. Experimental results show that bDNN-based VAD with the MRCG feature outperforms state-of-the-art VADs by a considerable margin. Copyright © 2014 ISCA.

关键词： Deep neural networks

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Binaural deep neural network classification for reverberant speech segregation 15

Binaural deep neural network classification for reverberant ...

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15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014

作者： Jiang, Yi Wang, DeLiang Liu, RunSheng Department of Electronic Engineering Tsinghua University Beijing100084 China Department of Computer Science and Engineering Center for Cognitive and Brain Sciences Ohio State University ColumbusOH43210 United States

While human listening is robust in complex auditory scenes, current speech segregation algorithms do not perform well in noisy and reverberant environments. This paper addresses the robustness in binaural speech segregation by employing binary classification based on deep neural networks (DNNs). We systematically examine DNN generalization to untrained configurations. Evaluations and comparisons show that DNN based binaural classification produces superior segregation performance in a variety of multisource and reverberant conditions. Copyright © 2014 ISCA.

关键词： Deep neural networks

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Grammatical error detection and correction using a single maximum entropy model 18

Grammatical error detection and correction using a single ma...

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18th Conference on Computational Natural Language Learning: Shared Task, CoNLL 2014

作者： Wang, Peilu Jia, Zhongye Zhao, Hai Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China

ISBN: (纸本)9781941643198

This paper describes the system of Shanghai Jiao Tong Unvierity team in the CoNLL-2014 shared task. Error correction operations are encoded as a group of predefined labels and therefore the task is formulized as a multi-label classification task. For training, labels are obtained through a strict rule-based approach. For decoding, errors are detected and corrected according to the classification results. A single maximum entropy model is used for the classification implementation incorporated with an improved feature selection algorithm. Our system achieved precision of 29.83, recall of 5.16 and F_0.5 of 15.24 in the official evaluation. © 2014 Association for Computational Linguistics.

关键词： Error correction

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Micro-expression Recognition Using Dynamic Textures on Tensor Independent Color Space

Micro-expression Recognition Using Dynamic Textures on Tenso...

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International Conference on Pattern Recognition

作者： Su-Jing Wang Wen-Jing Yan Xiaobai Li Guoying Zhao Xiaolan Fu 1) State Key Laboratory of Brain and Cognitive Science Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering University of Oulu Finland

ISBN: (纸本)9781479952106

Micro-expression is a brief involuntary facial expression which reveals genuine emotions and helps detect lies. It intrigues psychologists and computer scientists' (especially on computer vision and pattern recognition) interests due to its promising applications in various fields. Recent research reveals that color may provide useful information for expression recognition. In this paper, we propose a novel color space model, Tensor Independent Color Space (TICS), for enhancing the performance of micro-expression recognition. An micro-expression color video clip is treated as a fourth-order tensor, i.e. a four-dimension array. The first two dimensions are the spatial information, the third is the temporal information, and the fourth is the color information. We transform the fourth dimension from RGB into TICS, in which the color components are as independent as possible. The combination of dynamic texture in the independent color components can get higher accuracy than that in RGB. In addition, we define a set of Regions of Interest (ROIs) based on Facial Action Coding System (FACS) and calculated the dynamic texture histograms for each ROI. The experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that the performance in TICS is better than that in RGB or gray.

关键词： Tensile stress Color Image color analysis Face Databases Feature extraction Colored noise

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Group Activity Recognition with Group Interaction Zone

Group Activity Recognition with Group Interaction Zone

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International Conference on Pattern Recognition

作者： Young-Ji Kim Nam-Gyu Cho Seong-Whan Lee Department of Computer Science and Engineering Korea University Seoul Korea Department of Brain and Cognitive Engineering Korea University Seoul Korea

ISBN: (纸本)9781479952106

In this paper, we address the problem of recognizing group activities that include interactions between human objects based on their motion trajectory analysis. In order to resolve the complexity and ambiguity problems caused by a large number of human objects, we propose a Group Interaction Zone (GIZ) to detect meaningful groups in a scene so as to be robust against noisy information. Two novel features, Group Interaction Energy feature and Attraction and Repulsion Features, are proposed to better describe group activities within a GIZ. We demonstrate the effectiveness of our method with other methods on the public BEHAVE dataset.

关键词： Feature extraction Trajectory Conferences Pattern recognition computer vision Clustering algorithms Computational modeling

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An Improved Graph Model for Chinese Spell Checking 3

An Improved Graph Model for Chinese Spell Checking

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3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing, CLP 2014

作者： Xin, Yang Zhao, Hai Wang, Yuzhu Jia, Zhongye Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China

In this paper, we propose an improved graph model for Chinese spell checking. The model is based on a graph model for generic errors and two independentlytrained models for specific errors. First, a graph model represents a Chinese sentence and a modified single source shortest path algorithm is performed on the graph to detect and correct generic spelling errors. Then, we utilize conditional random fields to solve two specific kinds of common errors: the confusion of "?" (at) (pinyin is 'zai' in Chinese), "?" (again, more, then) (pinyin: zai) and "?" (of) (pinyin: de), "?" (-ly, adverb-forming particle) (pinyin: de), "?" (so that, have to) (pinyin: de). Finally, a rule based system is exploited to solve the pronoun usage confusions: "?" (she) (pinyin: ta), "?" (he) (pinyin: ta) and some others fixed collocation errors. The proposed model is evaluated on the standard data set released by the SIGHAN Bake-off 2014 shared task, and gives competitive result. © 2014 CLP 2014 - 3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing. All rights reserved.

关键词： Graph theory

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Learning hierarchical translation spans

Learning hierarchical translation spans

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2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014

作者： Zhang, Jingyi Utiyama, Masao Sumita, Eiichro Zhao, Hai Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Shanghai Jiao Tong Unviersity Shanghai200240 China Key Laboratory Shanghai Education Commission Intelligent Interaction Cognitive Engineering Shanghai Jiao Tong Unviersity Shanghai200240 China National Institute of Information and Communications Technology 3-5Hikaridai Keihanna Science City Kyoto619-0289 Japan

ISBN: (纸本)9781937284961

We propose a simple and effective approach to learn translation spans for the hierarchical phrase-based translation model. Our model evaluates if a source span should be covered by translation rules during decoding, which is integrated into the translation system as soft constraints. Compared to syntactic constraints, our model is directly acquired from an aligned parallel corpus and does not require parsers. Rich source side contextual features and advanced machine learning methods were utilized for this learning task. The proposed approach was evaluated on NTCIR-9 Chinese-English and Japanese-English translation tasks and showed significant improvement over the baseline system. © 2014 Association for Computational Linguistics.

关键词： Syntactics

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NEURAL NETWORKS FOR SUPERVISED PITCH TRACKING IN NOISE

NEURAL NETWORKS FOR SUPERVISED PITCH TRACKING IN NOISE

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kun Han DeLiang Wang Department of Computer Science and Engineering & Center for Cognitive and Brain Sciences The Ohio State University Columbus OH 43210-1277 USA

ISBN: (纸本)9781479928941

Determination of pitch in noise is challenging because of corrupted harmonic structure. In this paper, we extract pitch using supervised learning, where probabilistic pitch states are directly learned from noisy speech. We investigate two alternative neural networks modeling the pitch states given observations. The first one is the feedforward deep neural network (DNN), which is trained on static frame-level features. The second one is the recurrent deep neural network (RNN) capable of learning the temporal dynamics trained on sequential frame-level features. Both DNNs and RNNs produce accurate probabilistic outputs of pitch states, which are then connected into pitch contours by Viterbi decoding. Our systematic evaluation shows that the proposed pitch tracking approaches are robust to different noise conditions and significantly outperform current state-of-the-art pitch tracking techniques.

关键词： pitch PITCHES Noise Neural network Viterbi decoding recurrent neural nets Ship motion Semi-supervised Learning tracking

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