Cognition–284 Contents lists available at ScienceDirect Cognition journal homepage: www.elsevier.com/locate/COGNIT

Source URL: www-psych.stanford.edu

• Cognitive science
• Neuroscience
• Optical illusions
• Perception
• Cognitive neuroscience
• Vision
• Visual perception
• Motion aftereffect
• Illusory motion
• Motion perception
• Neural adaptation
• Mental image

www.nature.com/scientificreports OPEN No rapid audiovisual recalibration in adults on the autism spectrum

Source URL: win.pisavisionlab.org

• Nervous system
• Neuroscience
• Sensory systems
• Perception
• Autism
• Neurological disorders
• Neurology
• Sensory processing disorder
• Multisensory integration
• Sensory processing
• Neural coding
• Neural adaptation

COMBINING I-VECTOR REPRESENTATION AND STRUCTURED NEURAL NETWORKS FOR RAPID ADAPTATION Chunyang Wu, Penny Karanasou, Mark J.F. Gales Cambridge University Engineering Dept, Trumpington St., Cambridge, CB2 1PZ U.K. Email: {

Source URL: mi.eng.cam.ac.uk

• Machine learning
• Automatic identification and data capture
• Artificial intelligence
• Humancomputer interaction
• Markov models
• Artificial neural networks
• Computational neuroscience
• Computer accessibility
• Speech recognition
• Perceptron
• Speaker recognition
• Hidden Markov model

doi:j.neuron

Source URL: www.bcs.rochester.edu

• Neuroscience
• Nervous system
• Perception
• Cerebrum
• Cognitive neuroscience
• Cognition
• Sensory systems
• Categorical perception
• Speech perception
• Neural adaptation
• Functional magnetic resonance imaging
• Neural coding

DIFFERENTIABLE POOLING FOR UNSUPERVISED SPEAKER ADAPTATION Pawel Swietojanski and Steve Renals Centre for Speech Technology Research, University of Edinburgh, Edinburgh EH8 9AB {p.swietojanski, s.renals}@ed.ac.uk ABSTRA

Source URL: www.cstr.inf.ed.ac.uk

• Automatic identification and data capture
• Artificial neural networks
• Computational neuroscience
• Machine learning
• Computer accessibility
• Speech recognition
• Convolutional neural network
• Speaker recognition
• Neocognitron
• Unsupervised learning
• FMLLR
• Pattern recognition

A study of speaker adaptation for DNN-based speech synthesis Zhizheng Wu Pawel Swietojanski Christophe Veaux Steve Renals

Source URL: homepages.inf.ed.ac.uk

• Automatic identification and data capture
• Computational linguistics
• Computer accessibility
• Speech recognition
• Markov models
• Speech synthesis
• Speaker recognition
• Artificial neural network
• Hidden Markov model
• Vector
• Deep learning

Using NEAT for Continuous Adaptation and Teamwork Formation in Pacman Mark Wittkamp, Luigi Barone, Member, IEEE, and Philip Hingston, Senior Member, IEEE Abstract— Despite games often being used as a testbed for new co

Source URL: www.csse.uwa.edu.au

• Computational neuroscience
• Pac-Man
• Z80
• Arch Linux
• Ghost
• Escape
• Score
• Artificial neural network
• Artificial intelligence

Perception, 1980, volume 9, pagesThe movement aftereffect and a distribution-shift model for coding the direction of visual movement George Mather Department of Psychology, University of Reading, Earley Gate, W

Source URL: www.georgemather.com

• Perception
• Cognitive neuroscience
• Sensory systems
• Cognition
• Optical illusions
• Motion perception
• Neural adaptation
• Motion aftereffect
• Adaptation
• Action potential

Vision Research – 1802 Second-order processing of four-stroke apparent motion George Mather *, Linda Murdoch Experimental Psychology, School of Biology, Uni6ersity of Sussex, Brighton BN1 9QG, UK Receiv

Source URL: www.georgemather.com

• Perception
• Cognitive neuroscience
• Sensory systems
• Vision
• Cognition
• Motion perception
• Neural adaptation
• Texture
• Sound

MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Sequence Summarizing Neural Network for Speaker Adaptation Vesely, K.; Watanabe, S.; Zmolikova, K.; Karafiat, M.; Burget, L.; Cernocky, J.H.

Source URL: www.merl.com