Download or read book Unsupervised Learning of Invariant Object Representation in Primate Visual Cortex written by Nuo Li (Ph.D.) and published by . This book was released on 2011 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual object recognition (categorization and identification) is one of the most fundamental cognitive functions for our survival. Our visual system has the remarkable ability to convey to us visual object and category information in a manner that is largely tolerant ("invariant") to the exact position, size, pose of the object, illumination, and clutter. The ventral visual stream in non-human primate has solved this problem. At the highest stage of the visual hierarchy, the inferior temporal cortex (IT), neurons have selectivity for objects and maintain that selectivity across variations in the images. A reasonably sized population of these tolerant neurons can support object recognition. However, we do not yet understand how IT neurons construct this neuronal tolerance. The aim of this thesis is to tackle this question and to examine the hypothesis that the ventral visual stream may leverage experience to build its neuronal tolerance. One potentially powerful idea is that time can act as an implicit teacher, in that each object's identity tends to remain temporally stable, thus different retinal images of the same object are temporally contiguous. In theory, the ventral stream could take advantage of this natural tendency and learn to associate together the neuronal representations of temporally contiguous retinal images to yield tolerant object selectivity in IT cortex. In this thesis, I report neuronal support for this hypothesis in IT of non-human primates. First, targeted alteration of temporally contiguous experience with object images at different retinal positions rapidly reshaped IT neurons' position tolerance. Second, similar temporal contiguity manipulation of experience with object images at different sizes similarly reshaped IT size tolerance. These instances of experience-induced effect were similar in magnitude, grew gradually stronger with increasing visual experience, and the size of the effect was large. Taken together, these studies show that unsupervised, temporally contiguous experience can reshape and build at least two types of IT tolerance, and that they can do so under a wide range of spatiotemporal regimes encountered during natural visual exploration. These results suggest that the ventral visual stream uses temporal contiguity visual experience with a general unsupervised tolerance learning (UTL) mechanism to build its invariant object representation.
Download or read book Hierarchical Object Representations in the Visual Cortex and Computer Vision written by Antonio Rodríguez-Sánchez and published by Frontiers Media SA. This book was released on 2016-06-08 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past 40 years, neurobiology and computational neuroscience has proved that deeper understanding of visual processes in humans and non-human primates can lead to important advancements in computational perception theories and systems. One of the main difficulties that arises when designing automatic vision systems is developing a mechanism that can recognize - or simply find - an object when faced with all the possible variations that may occur in a natural scene, with the ease of the primate visual system. The area of the brain in primates that is dedicated at analyzing visual information is the visual cortex. The visual cortex performs a wide variety of complex tasks by means of simple operations. These seemingly simple operations are applied to several layers of neurons organized into a hierarchy, the layers representing increasingly complex, abstract intermediate processing stages. In this Research Topic we propose to bring together current efforts in neurophysiology and computer vision in order 1) To understand how the visual cortex encodes an object from a starting point where neurons respond to lines, bars or edges to the representation of an object at the top of the hierarchy that is invariant to illumination, size, location, viewpoint, rotation and robust to occlusions and clutter; and 2) How the design of automatic vision systems benefit from that knowledge to get closer to human accuracy, efficiency and robustness to variations.
Download or read book High level Visual Object Representation in Juvenile and Adult Primates written by Darren Allen Seibert and published by . This book was released on 2018 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite being reflexive, primate view invariant object recognition is a complex computational task. These computations are thought to reside in the ventral visual stream, specifically culminating in inferior temporal (IT) cortex. Recent research in machine learning has made great progress in modeling primate ventral visual stream computations. While the end result of current machine learning approaches produces models that are highly predictive of the adult state of the ventral stream, the learning approaches themselves are not biologically plausible, requiring tens of thousands to millions of human-labeled training points. Understanding primate visual development is therefore not only interesting from the perspective of neuroscience, but also has practical value in building more robust learning algorithms capable of functioning in domains where large amounts of human-labeled training information may be difficult or impossible to create. Better learning algorithms may also produce agents capable of adapting and behaving in the world not unlike humans. This thesis first describes work on predicting visual responses across the human ventral stream using convolutional neural networks (CNNs). We then describe a set of natural image statistics automatically incorporated into high-performing CNNs from supervised training-it is possible primate development incorporates these or similar natural image statistics into its synaptic strengths. Finally, we describe the first-large scale characterization of IT in 19-32 week old macaques. While we find longer response latencies in these younger animals, we do not find any differences in representation between adults and juveniles suggesting that, at 19-32 weeks of age, IT already supports robust object recognition consistent with adults. Our data provide an upper limit on the amount of training data needed to reach adult-level performance-approximately 2,800 hours of waking visual experience.
Download or read book Investigations of Factors that Affect Unsupervised Learning of 3D Object Representations written by Moqian Tian and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Humans have an amazing ability to learn to recognize objects across transformations that present very different retinal stimuli, such as changes in size, illumination, and rotations in space. Such identity-preserving image transformations (DiCarlo, Zoccolan, & Rust, 2012) put extraordinary pressure on our visual system because the computations needed to assign vastly different 2D images of an object to the same identity are non-trivial. However, both behavioral (Biederman & Cooper, 1991a, 1991b; Fiser & Biederman, 1995; Potter, 1976; Thorpe, Fize, & Marlot, 1996) and neural (Hung, Kreiman, Poggio, & DiCarlo, 2005) evidence suggest that the visual system solves this problem accurately and rapidly. While rotations in the image plane preserve the visible features, rotations in-depth may reveal new features of an opaque object and thus present the most difficult transformation for the visual system to resolve, because the resulting 2D image from an in-depth rotation may be unrecoverable from the original image. Thus, understanding how people achieve viewpoint invariance, or the ability to recognize objects from different views and rotations, is key to understanding the visual object recognition system. There is a general consensus that learning is an important component for developing viewpoint invariant object recognition (Logothetis and Pauls, 1992; Tarr and Pinker, 1989). Many studies show that learning can occur in an unsupervised way just from viewing example images of new objects (Edelman and Bulthoff, 1992; Tarr and Pinker, 1989). Two major theories regarding how the visual system achieves viewpoint invariance -- 3D-based theories (Biederman, 1987) and view-based theories (Ullman and Basri, 1989) -- recognize the importance of learning in achieving viewpoint invariant object recognition. However, they differ in what information is used during learning and what representation is consequently built. For example, view-based theories consider spatial and temporal continuities as necessary glue for linking multiple views of an object during unsupervised learning, but 3D-based theories consider feature information to be more important. They also differ on whether the object representation that is built after learning is 3D based or view based. To address these gaps in the published literature, I examined two core questions: What kind of spatial and temporal information in the visual input during unsupervised learning is critical for achieving viewpoint invariant recognition? And what kind of object representation is generated during the learning process? In Chapter 1, I will present a theoretical overview of the issues. Section 1 reviews theories and computational models of viewpoint invariant recognition, with a focus on the debate between 3D-based theories and view-based theories; Section 2 reviews psychophysical and neural evidence supporting each theory; and Section 3 discusses the predictions of the learning mechanisms of each of the competing theories. Chapter 2 presents results from a series of experiments that investigated the spatio-temporal information in the visual input during unsupervised learning that is key for learning the 3D structure of novel objects. Chapter 3 presents data from a series of experiments that examine how the format of the visual information during unsupervised learning affects learning the 3D structure of novel objects. Finally, in Chapter 4, I will discuss the theoretical implications of the findings presented in Chapters 2 & 3, and propose a new framework based on these results.
Download or read book Integrating Computational and Neural Findings in Visual Object Perception written by Judith C. Peters and published by Frontiers Media SA. This book was released on 2016-06-29 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Object Recognition in Man Monkey and Machine written by Michael J. Tarr and published by MIT Press. This book was released on 1999-03-15 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributors bring a wide range of methodologies to bear on the common problem of image-based object recognition. These interconnected essays on three-dimensional visual object recognition present cutting-edge research by some of the most creative neuroscientific, cognitive, and computational scientists in the field. Cassandra Moore and Patrick Cavanagh take a classic demonstration, the perception of "two-tone" images, and turn it into a method for understanding the nature of object representations in terms of surfaces and the interaction between bottom-up and top-down processes. Michael J. Tarr and Isabel Gauthier use computer graphics to study whether viewpoint-dependent recognition mechanisms can generalize between exemplars of perceptually defined classes. Melvyn A. Goodale and G. Keith Humphrey use innovative psychophysical techniques to investigate dissociable aspects of visual and spatial processing in brain-injured subjects. D.I. Perrett, M.W. Oram, and E. Ashbridge combine neurophysiological single-cell data from monkeys with computational analyses for a new way of thinking about the mechanisms that mediate viewpoint-dependent object recognition and mental rotation. Shimon Ullman also addresses possible mechanisms to account for viewpoint-dependent behavior, but from the perspective of machine vision. Finally, Philippe G. Schyns synthesizes work from many areas, to provide a coherent account of how stimulus class and recognition task interact. The contributors bring a wide range of methodologies to bear on the common problem of image-based object recognition.
Download or read book Invariant Recognition of Visual Objects written by Evgeniy Bart and published by Frontiers E-books. This book was released on with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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?
Download or read book Object Categorization written by Sven J. Dickinson and published by Cambridge University Press. This book was released on 2009-09-07 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique multidisciplinary perspective on the problem of visual object categorization.
Download or read book Memory Attention and Decision making written by Edmund T. Rolls and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Memory, attention, and decision-making are three major areas of psychology. They are frequently studied in isolation, and using a range of models to understand them. This book brings a unified approach to understanding these three processes. It shows how these fundamental functions forcognitive neuroscience can be understood in a common and unifying computational neuroscience framework. This framework links empirical research on brain function from neurophysiology, functional neuroimaging, and the effects of brain damage, to a description of how neural networks in the brainimplement these functions using a set of common principles. The book describes the principles of operation of these networks, and how they could implement such important functions as memory, attention, and decision-making.The topics covered includeThe hippocampus and memoryReward and punishment related learning: emotion and motivationVisual object recognition learningShort term memoryAttention, short term memory, and biased competitionProbabilistic decision-makingAction selectionDecision-makingAlso included are tutorial appendices onNeural networks in the brainNeural encoding in the brain'Memory, Attention and Decision-Making' will be valuable for those in the fields of neuroscience, psychology, and cognitive neuroscience from advanced undergraduate level upwards. It will also be of interest to those interested in neuroeconomics, animal behaviour, zoology, evolutionary biology,psychiatry, medicine, and philosophy. The book has been written with modular chapters and sections, making it possible to select particular Chapters for course work.
Download or read book Computational and Cognitive Neuroscience of Vision written by Qi Zhao and published by Springer. This book was released on 2016-10-03 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite a plethora of scientific literature devoted to vision research and the trend toward integrative research, the borders between disciplines remain a practical difficulty. To address this problem, this book provides a systematic and comprehensive overview of vision from various perspectives, ranging from neuroscience to cognition, and from computational principles to engineering developments. It is written by leading international researchers in the field, with an emphasis on linking multiple disciplines and the impact such synergy can lead to in terms of both scientific breakthroughs and technology innovations. It is aimed at active researchers and interested scientists and engineers in related fields.
Download or read book Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society written by Garrison W. Cottrell and published by Routledge. This book was released on 2019-02-21 with total page 904 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume features the complete text of all regular papers, posters, and summaries of symposia presented at the 18th annual meeting of the Cognitive Science Society. Papers have been loosely grouped by topic, and an author index is provided in the back. In hopes of facilitating searches of this work, an electronic index on the Internet's World Wide Web is provided. Titles, authors, and summaries of all the papers published here have been placed in an online database which may be freely searched by anyone. You can reach the Web site at: http://www.cse.ucsd.edu/events/cogsci96/proceedings. You may view the table of contents for this volume on the LEA Web site at: http://www.erlbaum.com.
Download or read book The Primate Visual System written by Jon H. Kaas and published by CRC Press. This book was released on 2003-07-28 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last 20 years of research have been marked by exceptional progress in understanding the organization and functions of the primate visual system. This understanding has been based on the wide application of traditional and newly emerging methods for identifying the functionally significant subdivisions of the system, their interconnections, the
Download or read book Primate Neuroethology written by Asif A. Ghazanfar and published by Oxford University Press, USA. This book was released on 2012-08-16 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume is the first of its kind to bridge the epistemological gap between primate ethologists and primate neurobiologists. Leading experts in several fields review work ranging from primate foraging behavior to the neurophysiology of motor control, from vocal communication to the functions of the auditory cortex.
Download or read book Cognitive and Neural Modelling for Visual Information Representation and Memorization written by Limiao Deng and published by CRC Press. This book was released on 2022-04-24 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on how visual information is represented, stored and extracted in the human brain, this book uses cognitive neural modeling in order to show how visual information is represented and memorized in the brain. Breaking through traditional visual information processing methods, the author combines our understanding of perception and memory from the human brain with computer vision technology, and provides a new approach for image recognition and classification. While biological visual cognition models and human brain memory models are established, applications such as pest recognition and carrot detection are also involved in this book. Given the range of topics covered, this book is a valuable resource for students, researchers and practitioners interested in the rapidly evolving field of neurocomputing, computer vision and machine learning.
Download or read book The Cognitive Neurosciences sixth edition written by David Poeppel and published by MIT Press. This book was released on 2020-04-21 with total page 1241 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixth edition of the foundational reference on cognitive neuroscience, with entirely new material that covers the latest research, experimental approaches, and measurement methodologies. Each edition of this classic reference has proved to be a benchmark in the developing field of cognitive neuroscience. The sixth edition of The Cognitive Neurosciences continues to chart new directions in the study of the biological underpinnings of complex cognition—the relationship between the structural and physiological mechanisms of the nervous system and the psychological reality of the mind. It offers entirely new material, reflecting recent advances in the field, covering the latest research, experimental approaches, and measurement methodologies. This sixth edition treats such foundational topics as memory, attention, and language, as well as other areas, including computational models of cognition, reward and decision making, social neuroscience, scientific ethics, and methods advances. Over the last twenty-five years, the cognitive neurosciences have seen the development of sophisticated tools and methods, including computational approaches that generate enormous data sets. This volume deploys these exciting new instruments but also emphasizes the value of theory, behavior, observation, and other time-tested scientific habits. Section editors Sarah-Jayne Blakemore and Ulman Lindenberger, Kalanit Grill-Spector and Maria Chait, Tomás Ryan and Charan Ranganath, Sabine Kastner and Steven Luck, Stanislas Dehaene and Josh McDermott, Rich Ivry and John Krakauer, Daphna Shohamy and Wolfram Schultz, Danielle Bassett and Nikolaus Kriegeskorte, Marina Bedny and Alfonso Caramazza, Liina Pylkkänen and Karen Emmorey, Mauricio Delgado and Elizabeth Phelps, Anjan Chatterjee and Adina Roskies
Download or read book Living machines written by Tony J. Prescott and published by Oxford University Press. This book was released on 2018-04-13 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contemporary research in science and engineering is seeking to harness the versatility and sustainability of living organisms. By exploiting natural principles, researchers hope to create new kinds of technology that are self-repairing, adaptable, and robust, and to invent a new class of machines that are perceptive, social, emotional, perhaps even conscious. This is the realm of the 'living machine'. Living machines can be divided into two types: biomimetic systems, that harness the principles discovered in nature and embody them in new artifacts, and biohybrid systems in which biological entities are coupled with synthetic ones. Living Machines: A handbook of research in biomimetic and biohybrid systems surveys this flourishing area of research, capturing the current state of play and pointing to the opportunities ahead. Promising areas in biomimetics include self-organization, biologically inspired active materials, self-assembly and self-repair, learning, memory, control architectures and self-regulation, locomotion in air, on land or in water, perception, cognition, control, and communication. Drawing on these advances the potential of biomimetics is revealed in devices that can harvest energy, grow or reproduce, and in animal-like robots that range from synthetic slime molds, to artificial fish, to humanoids. Biohybrid systems is a relatively new field, with exciting and largely unknown potential, but one that is likely to shape the future of humanity. This book surveys progress towards new kinds of biohybrid such as robots that merge electronic neurons with biological tissue, micro-scale machines made from living cells, prosthetic limbs with a sense of touch, and brain-machine interfaces that allow robotic devices to be controlled by human thought. The handbook concludes by exploring some of the impacts that living machine technologies could have on both society and the individual, exploring questions about how we will see and understand ourselves in a world in which the line between the natural and the artificial is increasingly blurred. With contributions from leading researchers from science, engineering, and the humanities, this handbook will be of broad interest to undergraduate and postgraduate students. Researchers in the areas of computational modeling and engineering, including artificial intelligence, machine learning, artificial life, biorobotics, neurorobotics, and human-machine interfaces will find Living Machines an invaluable resource.
Download or read book Visual Cortex and Deep Networks written by Tomaso A. Poggio and published by MIT Press. This book was released on 2016-09-23 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications. The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks—which do not reflect several important features of the ventral stream architecture and physiology—have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried out in the ventral stream. This book develops a mathematical framework that describes learning of invariant representations of the ventral stream and is particularly relevant to deep convolutional learning networks. The authors propose a theory based on the hypothesis that the main computational goal of the ventral stream is to compute neural representations of images that are invariant to transformations commonly encountered in the visual environment and are learned from unsupervised experience. They describe a general theoretical framework of a computational theory of invariance (with details and proofs offered in appendixes) and then review the application of the theory to the feedforward path of the ventral stream in the primate visual cortex.
