Integrating Computational And Neural Findings In Visual Object Perception
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Integrating Computational And Neural Findings In Visual Object Perception
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Author : Judith C. Peters
language : en
Publisher: Frontiers Media SA
Release Date : 2016-06-29
Integrating Computational And Neural Findings In Visual Object Perception written by Judith C. Peters and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-29 with Neurosciences. Biological psychiatry. Neuropsychiatry categories.
The articles in this Research Topic provide a state-of-the-art overview of the current progress in integrating computational and empirical research on visual object recognition. Developments in this exciting multidisciplinary field have recently gained momentum: High performance computing enabled breakthroughs in computer vision and computational neuroscience. In parallel, innovative machine learning applications have recently become available for datamining the large-scale, high resolution brain data acquired with (ultra-high field) fMRI and dense multi-unit recordings. Finally, new techniques to integrate such rich simulated and empirical datasets for direct model testing could aid the development of a comprehensive brain model. We hope that this Research Topic contributes to these encouraging advances and inspires future research avenues in computational and empirical neuroscience.
Integrating Computational And Neural Findings In Visual Object Perception
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Author :
language : en
Publisher:
Release Date : 2016
Integrating Computational And Neural Findings In Visual Object Perception written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
The articles in this Research Topic provide a state-of-the-art overview of the current progress in integrating computational and empirical research on visual object recognition. Developments in this exciting multidisciplinary field have recently gained momentum: High performance computing enabled breakthroughs in computer vision and computational neuroscience. In parallel, innovative machine learning applications have recently become available for datamining the large-scale, high resolution brain data acquired with (ultra-high field) fMRI and dense multi-unit recordings. Finally, new techniques to integrate such rich simulated and empirical datasets for direct model testing could aid the development of a comprehensive brain model. We hope that this Research Topic contributes to these encouraging advances and inspires future research avenues in computational and empirical neuroscience.
Integrating Visual System Mechanisms Computational Models And Algorithms Technologies
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Author : Hedva Spitzer
language : en
Publisher: Frontiers Media SA
Release Date : 2020-05-26
Integrating Visual System Mechanisms Computational Models And Algorithms Technologies written by Hedva Spitzer and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-26 with categories.
Information Routing Correspondence Finding And Object Recognition In The Brain
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Author : Philipp Wolfrum
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-09-08
Information Routing Correspondence Finding And Object Recognition In The Brain written by Philipp Wolfrum and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-08 with Technology & Engineering categories.
At?rstsight,thisbookisaboutfacerecognitioninthebrain.Itsmorelasting value, however,lies in the paradigmatic way in which this particular problem is treated. From the basic ideas that are worked out here in concrete detail, it is a naturaland simple next step to at leastimagine, if not realizein model form, much more generalstructures and processes,thus helping to bridge the still tremendous chasm between mind and brain. It is the purpose of this foreword to point out these generic traits. For centuries, thinking about the brain has been dominated by the most complexmechanisticdevicesofthetime,clockwork,communicatinghydraulic tubesor,today,thecomputer.Thecomputer,takenasincarnationoftheU- versal Turing Machine, can implement any conceivable process, so that also a functional brain can surely be simulated on it, an idea that, beginning in the ?fties of the last century, has been seducing scientists to create “art- cial intelligence” in the computer. As a result we now have an information technology that displays many functional capabilities formerly regarded as the exclusive domain of the mind. As fascinating as this is, doting on “int- ligent machines” is systematically diverting our attention awayfrom the true problems of understanding the working of the brain.
The Embodied Brain Computational Mechanisms Of Integrated Sensorimotor Interactions With A Dynamic Environment
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Author : Mario Senden
language : en
Publisher: Frontiers Media SA
Release Date : 2020-07-31
The Embodied Brain Computational Mechanisms Of Integrated Sensorimotor Interactions With A Dynamic Environment written by Mario Senden and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-31 with categories.
Computational Models Of Brain And Behavior
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Author : Ahmed A. Moustafa
language : en
Publisher: John Wiley & Sons
Release Date : 2017-11-13
Computational Models Of Brain And Behavior written by Ahmed A. Moustafa and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-13 with Psychology categories.
A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.
Findings And Current Opinion In Cognitive Neuroscience
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Author : Larry R. Squire
language : en
Publisher: MIT Press
Release Date : 1998
Findings And Current Opinion In Cognitive Neuroscience written by Larry R. Squire and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Medical categories.
This volume, which contains forty-six review articles from recent issues of Current Opinion in Neurobiology, provides easy access to the current state of theory and findings in the field.
Multisensory Integration In Action Control
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Author : Jochen Musseler
language : en
Publisher: Frontiers E-books
Release Date : 2014-11-03
Multisensory Integration In Action Control written by Jochen Musseler and has been published by Frontiers E-books this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-03 with Perception categories.
The integration of multisensory information is an essential mechanism in perception and in controlling actions. Research in multisensory integration is concerned with how the information from the different sensory modalities, such as the senses of vision, hearing, smell, taste, touch, and proprioception, are integrated to a coherent representation of objects. Multisensory integration is central for action control. For instance, when you grasp for a rubber duck, you can see its size and hear the sound it produces. Moreover, identical physical properties of an object can be provided by different senses. You can both see and feel the size of the rubber duck. Even when you grasp for the rubber duck with a tool (e.g. with tongs), the information from the hand, from the effect points of the tool and from the eyes are integrated in a manner to act successfully. Over the recent decade a surge of interest in multisensory integration and action control has been witnessed, especially in connection with the idea that multiple sensory sources are integrated in an optimized way. For this perspective to mature, it will be helpful to delve deeper into the information processing mechanisms and their neural correlates, asking about the range and constraints of this mechanisms, about its localization and involved networks.
Sensory Cue Integration
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Author : Julia Trommershauser
language : en
Publisher:
Release Date : 2011
Sensory Cue Integration written by Julia Trommershauser and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Medical categories.
This book provides an introduction into both computational models and experimental paradigms that are concerned with sensory cue integration both within and between sensory modalities. Importantly, across behavioral, electrophysiological and theoretical approaches, Bayesian statistics is emerging as a common language in which cue-combination problems can be expressed. This book focuses on the emerging probabilistic way of thinking about these problems. These approaches derive from the realization that all our sensors are noisy and moreover are often affected by ambiguity. For example, mechanoreceptor outputs are variable and they cannot distinguish if a perceived force is caused by the weight of an object or by force we are producing ourselves. The computational approaches described in this book aim at formalizing the uncertainty of cues. They describe cue combination as the nervous system's attempt to minimize uncertainty in its estimates and to choose successful actions. Some computational approaches described in the chapters of this book are concerned with the application of such statistical ideas to real-world cue-combination problems, such as shape and depth perception. Other parts of the book ask how uncertainty may be represented in the nervous system and used for cue combination. The broadening scope of probabilistic approaches to cue combination is highlighted in the breadth of topics covered in this book: the chapters summarize and discuss computational approaches and behavioral evidence aimed at understanding the combination of visual, auditory, proprioceptive, and haptic cues. Some chapters address the combination of cues within a single sensory modality while others address the combination across sensory modalities. Neural implementation, behavior, and theory are considered. The unifying aspect of this book is the focus on the uncertainty intrinsic to sensory cues and the underlying question of how the nervous system deals with this uncertainty. The book is intended as a reference text for graduate students and professionals in perceptual psychology, computational neuroscience, cognitive neuroscience and sensory neurophysiology.
Invariant Recognition Of Visual Objects
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Author : Evgeniy Bart
language : en
Publisher: Frontiers E-books
Release Date :
Invariant Recognition Of Visual Objects written by Evgeniy Bart and has been published by Frontiers E-books this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
This Research Topic will focus on how the visual system recognizes objects regardless of variations in the viewpoint, illumination, retinal size, background, etc. Contributors are encouraged to submit articles describing novel results, models, viewpoints, perspectives and/or methodological innovations relevant to this topic. The issues we wish to cover include, but are not limited to, perceptual invariance under one or more of the following types of image variation: • Object shape • Task • Viewpoint (from the translation and rotation of the object relative to the viewer) • Illumination, shading, and shadows • Degree of occlusion • Retinal size • Color • Surface texture • Visual context, including background clutter and crowding • Object motion (including biological motion). Examples of questions that are particularly interesting in this context include, but are not limited to: • Empirical characterizations of properties of invariance: does invariance always exist? How wide is its range and how strong is the tolerance to viewing conditions within this range? • Invariance in naïve vs. experienced subjects: Is invariance built-in or learned? How can it be learned, under which conditions and how effectively? Is it learned incidentally, or are specific task and reward structures necessary for learning? How is generalizability and transfer of learning related to the generalizability/invariance of perception? • Invariance during inference: Are there conditions (e.g. fast presentation time or otherwise resource-constrained recognition) when invariance breaks? • What are some plausible computational or neural mechanisms by which invariance could be achieved?