Download or read book Computational Models of Primary Visual Cortex and the Structure of Natural Images written by Hauke Bartsch and published by . This book was released on 2004 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Natural Image Statistics written by Aapo Hyvärinen and published by Springer Science & Business Media. This book was released on 2009-04-21 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aims and Scope This book is both an introductory textbook and a research monograph on modeling the statistical structure of natural images. In very simple terms, “natural images” are photographs of the typical environment where we live. In this book, their statistical structure is described using a number of statistical models whose parameters are estimated from image samples. Our main motivation for exploring natural image statistics is computational m- eling of biological visual systems. A theoretical framework which is gaining more and more support considers the properties of the visual system to be re?ections of the statistical structure of natural images because of evolutionary adaptation processes. Another motivation for natural image statistics research is in computer science and engineering, where it helps in development of better image processing and computer vision methods. While research on natural image statistics has been growing rapidly since the mid-1990s, no attempt has been made to cover the ?eld in a single book, providing a uni?ed view of the different models and approaches. This book attempts to do just that. Furthermore, our aim is to provide an accessible introduction to the ?eld for students in related disciplines.

Download or read book Computational Maps in the Visual Cortex written by Risto Miikkulainen and published by Springer Science & Business Media. This book was released on 2006-01-16 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: For more than 30 years, the visual cortex has been the source of new theories and ideas about how the brain processes information. The visual cortex is easily accessible through a variety of recording and imagining techniques and allows mapping of high level behavior relatively directly to neural mechanisms. Understanding the computations in the visual cortex is therefore an important step toward a general theory of computational brain theory.

Download or read book Developmental Stereo written by Mojtaba Solgi and published by ProQuest. This book was released on 2009 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computational Models of Visual Processing written by Michael S. Landy and published by MIT Press. This book was released on 1991 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: The more than twenty contributions in this book, all new and previously unpublished, provide an up-to-date survey of contemporary research on computational modeling of the visual system. The approaches represented range from neurophysiology to psychophysics, and from retinal function to the analysis of visual cues to motion, color, texture, and depth. The contributions are linked thematically by a consistent consideration of the links between empirical data and computational models in the study of visual function. An introductory chapter by Edward Adelson and James Bergen gives a new and elegant formalization of the elements of early vision. Subsequent sections treat receptors and sampling, models of neural function, detection and discrimination, color and shading, motion and texture, and 3D shape. Each section is introduced by a brief topical review and summary. ContributorsEdward H. Adelson, Albert J. Ahumada, Jr., James R. Bergen, David G. Birch, David H. Brainard, Heinrich H. Bülthoff, Charles Chubb, Nancy J. Coletta, Michael D'Zmura, John P. Frisby, Norma Graham, Norberto M. Grzywacz, P. William Haake, Michael J. Hawken, David J. Heeger, Donald C. Hood, Elizabeth B. Johnston, Daniel Kersten, Michael S. Landy, Peter Lennie, J. Stephen Mansfield, J. Anthony Movshon, Jacob Nachmias, Andrew J. Parker, Denis G. Pelli, Stephen B. Pollard, R. Clay Reid, Robert Shapley, Carlo L. M. Tiana, Brian A. Wandell, Andrew B. Watson, David R. Williams, Hugh R. Wilson, Yuede. Yang, Alan L. Yuille

Download or read book Computational Models of Early Visual Processing Layers written by Honghao Shan and published by . This book was released on 2010 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual information passes through layers of processing along the visual pathway, such as retina, lateral geniculate nucleus (LGN), primary visual cortex (V1), prestriate cortex (V2), and beyond. Understanding the functional roles of these visual processing layers will not only help to understand psychophysical and neuroanatomical observations of these layers, but also would help to build intelligent computer vision systems that exhibit human-like behaviors and performance. One of the popular theories about the functional role of visual perception, the efficient coding theory, hypothesizes that the early visual processing layers serve to capture the statistical structure of the visual inputs by removing the redundancy in the visual outputs. Linear implementations of the efficient coding theory, such as independent component analysis (ICA) and sparse coding, learn visual features exhibiting the receptive field properties of V1 simple cells when they are applied to grayscale image patches. In this dissertation, we explore different aspects of the early visual processing layers by building computational models following the efficient coding theory. 1) We develop a hierarchical model, Recursive ICA, that captures nonlinear statistical structures of the visual inputs that cannot be captured by a single layer of ICA. The model is motivated by the idea that higher layers of the visual pathway, such as V2, might work under similar computational principles as the primary visual cortex. Hence we apply a second layer of ICA on top of the first layer ICA outputs. To allow the second layer of ICA to better capture nonlinear statistical structures, we derive a coordinate-wise nonlinear activation function that transforms the first layer ICA's outputs to the second layer ICA's inputs. When applied to grayscale image patches, the model's second layer learns nonlinear visual features, such as texture boundaries and shape contours. We apply the above model to natural scene images, such as forest and grassland, to learn some generic visual features, and then use these features for face and handwritten digit recognition. We get higher recognition rates than those systems built with features designed for face and digit recognition. (2) We show that retinal coding, the pre-cortical stage of visual processing, can also be explained by the efficient coding theory. The retinal coding model turns out to be another variation of Sparse PCA, a technique widely applied in signal processing, financial analysis, bioinformatics, etc. Compared with ICA, which removes the redundancy among the input dimensions, Sparse PCA removes redundancy among the input samples. We apply Sparse PCA to grayscale images, chromatic images, grayscale videos, environmental sound, and human speech, and learn visual and auditory features that exhibit the filtering properties of retinal ganglion cells and auditory nerve fibers. This work suggests that the pre-cortical stages of visual and auditory pathway might work under similar computational principles.

Download or read book Visual Population Codes written by Nikolaus Kriegeskorte and published by MIT Press. This book was released on 2012 with total page 659 pages. Available in PDF, EPUB and Kindle. Book excerpt: How visual content is represented in neuronal population codes and how to analyze such codes with multivariate techniques. Vision is a massively parallel computational process, in which the retinal image is transformed over a sequence of stages so as to emphasize behaviorally relevant information (such as object category and identity) and deemphasize other information (such as viewpoint and lighting). The processes behind vision operate by concurrent computation and message passing among neurons within a visual area and between different areas. The theoretical concept of "population code" encapsulates the idea that visual content is represented at each stage by the pattern of activity across the local population of neurons. Understanding visual population codes ultimately requires multichannel measurement and multivariate analysis of activity patterns. Over the past decade, the multivariate approach has gained significant momentum in vision research. Functional imaging and cell recording measure brain activity in fundamentally different ways, but they now use similar theoretical concepts and mathematical tools in their modeling and analyses. With a focus on the ventral processing stream thought to underlie object recognition, this book presents recent advances in our understanding of visual population codes, novel multivariate pattern-information analysis techniques, and the beginnings of a unified perspective for cell recording and functional imaging. It serves as an introduction, overview, and reference for scientists and students across disciplines who are interested in human and primate vision and, more generally, in understanding how the brain represents and processes information.

Download or read book Exploration of Cortical Function written by M. Stetter and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploration of Cortical Function summarizes recent research efforts aiming at the revelation of cortical population coding and signal processing strategies. Topics include optical detection techniques of population activity in the sub-millimeter range, advanced methods for the statistical analysis of these data, and biologically inspired neuronal modeling techniques for population activities in the frameworks of optimal coding, statistical learning theory, and mean-field recurrent networks. Exploration of Cortical Function is unique in that it covers one complete branch of population-based brain research ranging from techniques for data acquisition over data analysis up to modeling techniques for the quantification of functional principles. The volume covers an area which is of great current interest to researchers working on cerebral cortex. The combination of models and image analysis techniques to examine the activity of large cohorts of neurons is especially intriguing and prone to considerable error and debate.

Download or read book Neuromathematics of Vision written by Giovanna Citti and published by Springer Science & Business Media. This book was released on 2014-02-08 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to the study of the functional architecture of the visual cortex. Its geometrical structure is the differential geometry of the connectivity between neural cells. This connectivity is building and shaping the hidden brain structures underlying visual perception. The story of the problem runs over the last 30 years, since the discovery of Hubel and Wiesel of the modular structure of the primary visual cortex, and slowly cams towards a theoretical understanding of the experimental data on what we now know as functional architecture of the primary visual cortex. Experimental data comes from several domains: neurophysiology, phenomenology of perception and neurocognitive imaging. Imaging techniques like functional MRI and diffusion tensor MRI allow to deepen the study of cortical structures. Due to this variety of experimental data, neuromathematematics deals with modelling both cortical structures and perceptual spaces. From the mathematical point of view, neuromathematical call for new instruments of pure mathematics: sub-Riemannian geometry models horizontal connectivity, harmonic analysis in non commutative groups allows to understand pinwheels structure, as well as non-linear dimensionality reduction is at the base of many neural morphologies and possibly of the emergence of perceptual units. But at the center of the neurogeometry is the problem of harmonizing contemporary mathematical instruments with neurophysiological findings and phenomenological experiments in an unitary science of vision. The contributions to this book come from the very founders of the discipline.

Download or read book Visual Cortex written by Stephane Molotchnikoff and published by BoD – Books on Demand. This book was released on 2012-09-26 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: The neurosciences have experienced tremendous and wonderful progress in many areas, and the spectrum encompassing the neurosciences is expansive. Suffice it to mention a few classical fields: electrophysiology, genetics, physics, computer sciences, and more recently, social and marketing neurosciences. Of course, this large growth resulted in the production of many books. Perhaps the visual system and the visual cortex were in the vanguard because most animals do not produce their own light and offer thus the invaluable advantage of allowing investigators to conduct experiments in full control of the stimulus. In addition, the fascinating evolution of scientific techniques, the immense productivity of recent research, and the ensuing literature make it virtually impossible to publish in a single volume all worthwhile work accomplished throughout the scientific world. The days when a single individual, as Diderot, could undertake the production of an encyclopedia are gone forever. Indeed most approaches to studying the nervous system are valid and neuroscientists produce an almost astronomical number of interesting data accompanied by extremely worthy hypotheses which in turn generate new ventures in search of brain functions. Yet, it is fully justified to make an encore and to publish a book dedicated to visual cortex and beyond. Many reasons validate a book assembling chapters written by active researchers. Each has the opportunity to bind together data and explore original ideas whose fate will not fall into the hands of uncompromising reviewers of traditional journals. This book focuses on the cerebral cortex with a large emphasis on vision. Yet it offers the reader diverse approaches employed to investigate the brain, for instance, computer simulation, cellular responses, or rivalry between various targets and goal directed actions. This volume thus covers a large spectrum of research even though it is impossible to include all topics in the extremely diverse field of neurosciences.

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 Computational Models of Contrast and Orientation Processing in Primary Visual Cortex written by and published by . This book was released on 2011 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computational Neuroanatomy written by Giorgio A. Ascoli and published by Springer Science & Business Media. This book was released on 2002-07-01 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Computational Neuroanatomy: Principles and Methods, the path-breaking investigators who founded the field review the principles and key techniques available to begin the creation of anatomically accurate and complete models of the brain. Combining the vast, data-rich field of anatomy with the computational power of novel hardware, software, and computer graphics, these pioneering investigators lead the reader from the subcellular details of dendritic branching and firing to system-level assemblies and models.

Download or read book Integration of Natural Language and Vision Processing written by Paul Mc Kevitt and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although there has been much progress in developing theories, models and systems in the areas of Natural Language Processing (NLP) and Vision Processing (VP) there has heretofore been little progress on integrating these subareas of Artificial Intelligence (AI). This book contains a set of edited papers addressing computational models and systems for the integration of NLP and VP. The papers focus on site descriptions such as that of the large Japanese $500 million Real World Computing (RWC) project, on historical philosophical issues, on systems which have been built and which integrate the processing of visual scenes together with language about them, and on spatial relations which appear to be the key to integration. The U.S.A., Japan and the EU are well reflected, showing up the fact that integration is a truly international issue. There is no doubt that all of this will be necessary for the InformationSuperHighways of the future.

Download or read book The Mechanisms of Reliable Coding in Mouse Visual Cortex written by Rajeev Vijay Rikhye and published by . This book was released on 2016 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: As we interact with the environment, our senses are constantly bombarded with information. Neurons in the visual cortex have to transform these complex inputs into robust and parsimonious neural codes that effectively guide behavior. The ability of neurons to efficiently convey information is, however, limited by intrinsic and shared variability. Despite this limitation, neurons in primary visual cortex (V1) are able to respond with high fidelity to relevant stimuli. My thesis proposes that high fidelity encoding can be achieved by dynamically increasing trial-to-trial response reliability. In particular, in this thesis, I use the mouse primary visual cortex (V1) as a model to understand how reliable coding arises, and why it is important for visual perception. Using a combination of novel experimental and computational techniques, my thesis identifies three main factors that can modulate intrinsic variability. My first goal was to understand the extrinsic, stimulus-dependent, factors responsible for reliably coding (Chapter 3). Natural scenes contain unique statistical properties that could be leveraged by the visual cortex for efficient coding. Thus, the first aim is to elucidate how image statistics modulate reliable coding in V1. To this end, I developed a novel noise masking procedure that allowed us to specifically perturb the spectral content of natural movies without altering the edges. Using high-speed twophoton calcium imaging in mice, I discovered that movies with stronger spatial correlations are more reliably processed by V1 neurons than movies lacking these correlations. In particular, perturbing spatial correlations in the movie dynamically altered the structure of interneuronal correlations. Movies with more naturalistic correlations typically recruited large neuronal ensembles that were weakly noise correlated. Using computational modeling, I discovered that these ensembles were able reduce shared noise through divisive normalization. Together, these findings demonstrate that natural scene statistics dynamically recruit neuronal ensembles to ensure reliable coding. Microcircuits of inhibitory interneurons lie at the heart of all cortical computations. It has been proposed that these interneurons are responsible for reliable spiking by controlling the temporal window over which synaptic inputs are integrated. However, no study has yet conclusively investigated the role of different interneuron subtypes. Thus, my second goal was to establish how natural scenes are reliably encoded by dissecting the inhibitory mechanisms underlying reliable coding (Chapter 4). Specifically, I investigated the role of somatostatin-expressing dendrite targeting interneurons (SST) and parvalbumin-expressing soma targeting interneurons (PV), which are known to provide distinct forms of inhibition onto pyramidal neurons. Using a novel combination of dual-color calcium imaging and optogenetic manipulation, I have discovered that the SST->PV inhibitory circuit plays a crucial role in modulating pyramidal cell reliability. In particular, by transiently suppressing PV neurons, SST neurons are able to route inhibition rapidly from the soma to the dendrites. Strong dendritic inhibition allows noisy inputs to be filtered out by the dendrites, while weaker somatic inhibition allows these inputs to be integrated to produce reliable spikes. In agreement with these results, I found that selectively deleting MeCP2 from these interneurons resulted in unreliable visual processing and other circuit-specific deficits, which are commonly observed in Rett Syndrome (Chapter 5). These results underscore the importance of intact inhibitory microcircuits in reliable processing. Finally, my goal was to determine why reliable coding is necessary for visual processing (Chapter 6). To this end, I trained head-fixed mice to perform a natural movie discrimination task. Mice were able to learn how to discriminate between two movies after a short training period. By perturbing the amplitude spectrum of these movies, I discovered that mice used structural information in the phase spectrum to discriminate between the different movies. This suggests that mice also use similar strategies as higher mammals for scene recognition. Inspired by this result, we trained mice on a harder target categorization task, where mice had to identify the movies from an ensemble that were more similar to the target movie to gain a water reward. We developed this movie ensemble by blending together the phase spectrum of a target and non-target movie at different fractions. Optically activating SST neurons in V1 improved the ability of mice to correctly identify "target-like" movies. This increase in behavioral performance correlated well with an increase in V1 coding reliability. Thus, reliable codes are a prerequisite for accurate visual perception. Taken together, this work bridges the gap between cells, circuits and behavior, and provides mechanistic insight into how complex visual stimuli are encoded with high fidelity in the visual cortex.

Download or read book The Encoding of Natural Images in Human Visual Cortex written by Kendrick Norris Kay and published by . This book was released on 2009 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Elements of Neurogeometry written by Jean Petitot and published by Springer. This book was released on 2017-11-08 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes several mathematical models of the primary visual cortex, referring them to a vast ensemble of experimental data and putting forward an original geometrical model for its functional architecture, that is, the highly specific organization of its neural connections. The book spells out the geometrical algorithms implemented by this functional architecture, or put another way, the “neurogeometry” immanent in visual perception. Focusing on the neural origins of our spatial representations, it demonstrates three things: firstly, the way the visual neurons filter the optical signal is closely related to a wavelet analysis; secondly, the contact structure of the 1-jets of the curves in the plane (the retinal plane here) is implemented by the cortical functional architecture; and lastly, the visual algorithms for integrating contours from what may be rather incomplete sensory data can be modelled by the sub-Riemannian geometry associated with this contact structure. As such, it provides readers with the first systematic interpretation of a number of important neurophysiological observations in a well-defined mathematical framework. The book’s neuromathematical exploration appeals to graduate students and researchers in integrative-functional-cognitive neuroscience with a good mathematical background, as well as those in applied mathematics with an interest in neurophysiology.