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Non Linear Analysis Of Visual Cortical Neurons
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Book Synopsis Non-linear Analysis of Visual Cortical Neurons by : Lowell D. Jacobson
Download or read book Non-linear Analysis of Visual Cortical Neurons written by Lowell D. Jacobson and published by . This book was released on 1990 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the first reporting period, new equipment was purchased and set up and new software was developed in preparation for electrophysiological experiments to study the neural networks that underly the binocular non-linear filtering properties of cells in the monkey (Macaca fascicularis) visual cortex. This preparatory task was completed. In addition, new methods were developed for using input-output measurements to identify multi-input nonlinear systems. These new mathematical results have been written up and accepted for journal publication and presented at two conferences. In addition, the new system identification methods have been applied in preliminary analyses of previously obtained monocular stimulus-response data. Keywords: Binocular vision; Nonlinear system identification; Neural network. (JHD).
Book Synopsis Non-Linear Analysis of Visual Cortical Neurons by :
Download or read book Non-Linear Analysis of Visual Cortical Neurons written by and published by . This book was released on 1992 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative procedures were developed for testing block-structured models for multi-input nonlinear visual circuits studied with spatiotemporal white noise. A linear-nonlinear (LN) model test index was found to be suitable for classifying cells as simple versus complex. Although simple cells were better modeled as LN systems than complex cells, most simple cells deviated considerably from LN behavior. A nonlinearity of cortical origin would appear to be responsible, possibly activated more strongly by broadband noise than by sinewave grating stimuli. Also, two classes of binocular complex cells were identified. Whereas all binocular complex cells necessarily have a non-zero second-order same-eye interaction kernel, their second-order cross-eye interaction kernel could, it was found, be either non-zero or identically zero. Binocular vision, nonlinear system identification, neural network.
Book Synopsis Nonlinear Analysis of Complex Cells in Primary Visual Cortex by : Jonathan Olin Vahram Touryan
Download or read book Nonlinear Analysis of Complex Cells in Primary Visual Cortex written by Jonathan Olin Vahram Touryan and published by . This book was released on 2004 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonlinear Analysis of Binocular Neurons in the Primary Visual Cortex by : Akiyuki Anzai
Download or read book Nonlinear Analysis of Binocular Neurons in the Primary Visual Cortex written by Akiyuki Anzai and published by . This book was released on 1997 with total page 604 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multi-input Nonlinear Systems Analyses of Primate Visual Cortical Neurons by : Hai-Wen Chen
Download or read book Multi-input Nonlinear Systems Analyses of Primate Visual Cortical Neurons written by Hai-Wen Chen and published by . This book was released on 1992 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Linear and Nonlinear Mechanisms of Neuronal Function in the Visual Cortex by : Gregory Clarke DeAngelis
Download or read book Linear and Nonlinear Mechanisms of Neuronal Function in the Visual Cortex written by Gregory Clarke DeAngelis and published by . This book was released on 1992 with total page 770 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks by : Robert B. Pinter
Download or read book Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks written by Robert B. Pinter and published by CRC Press. This book was released on 2018-05-04 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text brings to vision research a treatment different from that often found in books on the subject in its emphasis on nonlinear aspects of vision, from human perception to eye cells of the fly. There is considerable emphasis on mathematics, which forms not only models but the algorithms for processing data.
Book Synopsis Nonlinear Spatial Interactions of Visual Cortical Neurons by : Harry Solomon Pearle
Download or read book Nonlinear Spatial Interactions of Visual Cortical Neurons written by Harry Solomon Pearle and published by . This book was released on 1971 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonlinear Analysis of the Neural Basis for Natural Vision by : Stephen Vaclav David
Download or read book Nonlinear Analysis of the Neural Basis for Natural Vision written by Stephen Vaclav David and published by . This book was released on 2004 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Higher-Order Processing in the Visual System by : Gregory R. Bock
Download or read book Higher-Order Processing in the Visual System written by Gregory R. Bock and published by John Wiley & Sons. This book was released on 2008-04-30 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foremost neurophysiologists and psychophysicists provide pertinent information on the nature of representation at the earliest stages as this will constrain the disposition of all subsequent processing. This processing is discussed in several different types of visual perception.
Book Synopsis Nonlinear Analysis in Neuroscience and Behavioral Research by : Tobias A. Mattei
Download or read book Nonlinear Analysis in Neuroscience and Behavioral Research written by Tobias A. Mattei and published by Frontiers Media SA. This book was released on 2016-10-31 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although nonlinear dynamics have been mastered by physicists and mathematicians for a long time (as most physical systems are inherently nonlinear in nature), the recent successful application of nonlinear methods to modeling and predicting several evolutionary, ecological, physiological, and biochemical processes has generated great interest and enthusiasm among researchers in computational neuroscience and cognitive psychology. Additionally, in the last years it has been demonstrated that nonlinear analysis can be successfully used to model not only basic cellular and molecular data but also complex cognitive processes and behavioral interactions. The theoretical features of nonlinear systems (such unstable periodic orbits, period-doubling bifurcations and phase space dynamics) have already been successfully applied by several research groups to analyze the behavior of a variety of neuronal and cognitive processes. Additionally the concept of strange attractors has lead to a new understanding of information processing which considers higher cognitive functions (such as language, attention, memory and decision making) as complex systems emerging from the dynamic interaction between parallel streams of information flowing between highly interconnected neuronal clusters organized in a widely distributed circuit and modulated by key central nodes. Furthermore, the paradigm of self-organization derived from the nonlinear dynamics theory has offered an interesting account of the phenomenon of emergence of new complex cognitive structures from random and non-deterministic patterns, similarly to what has been previously observed in nonlinear studies of fluid dynamics. Finally, the challenges of coupling massive amount of data related to brain function generated from new research fields in experimental neuroscience (such as magnetoencephalography, optogenetics and single-cell intra-operative recordings of neuronal activity) have generated the necessity of new research strategies which incorporate complex pattern analysis as an important feature of their algorithms. Up to now nonlinear dynamics has already been successfully employed to model both basic single and multiple neurons activity (such as single-cell firing patterns, neural networks synchronization, autonomic activity, electroencephalographic measurements, and noise modulation in the cerebellum), as well as higher cognitive functions and complex psychiatric disorders. Similarly, previous experimental studies have suggested that several cognitive functions can be successfully modeled with basis on the transient activity of large-scale brain networks in the presence of noise. Such studies have demonstrated that it is possible to represent typical decision-making paradigms of neuroeconomics by dynamic models governed by ordinary differential equations with a finite number of possibilities at the decision points and basic heuristic rules which incorporate variable degrees of uncertainty. This e-book has include frontline research in computational neuroscience and cognitive psychology involving applications of nonlinear analysis, especially regarding the representation and modeling of complex neural and cognitive systems. Several experts teams around the world have provided frontline theoretical and experimental contributions (as well as reviews, perspectives and commentaries) in the fields of nonlinear modeling of cognitive systems, chaotic dynamics in computational neuroscience, fractal analysis of biological brain data, nonlinear dynamics in neural networks research, nonlinear and fuzzy logics in complex neural systems, nonlinear analysis of psychiatric disorders and dynamic modeling of sensorimotor coordination. Rather than a comprehensive compilation of the possible topics in neuroscience and cognitive research to which non-linear may be used, this e-book intends to provide some illustrative examples of the broad range of
Book Synopsis Vision: From Neurons to Cognition by : C. Casanova
Download or read book Vision: From Neurons to Cognition written by C. Casanova and published by Gulf Professional Publishing. This book was released on 2001-10-25 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: Internationally renowned researchers discuss how the various parts of the brain process and integrate visual signals, providing up to date original findings, reviews, and theoretical proposals on visual processing. This book addresses the basic mechanisms of visual perception as well as issues such as neuronal plasticity, functional reorganization and recovery, residual vision, and sensory substitution. Knowledge of the basic mechanisms by which our brain can analyze, reconstruct, and interpret images in the external world is of fundamental importance for our capacity to understand the nature and causes of visual deficits, such as those resulting from ischemia, abnormal development, neuro-degenerative disorders, and normal aging. It is also essential to our goal of developing better therapeutic strategies, such as early diagnosis, visual training, behavioral rehabilitation of visual functions, and visual implants.
Book Synopsis Models of the Visual Cortex by : David Rose
Download or read book Models of the Visual Cortex written by David Rose and published by John Wiley & Sons. This book was released on 1985 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and stimulating study which presents the views of 71 leading theorists on the underlying mechanisms and functions of the primary visual cortex.
Book Synopsis Parallel Processing in the Visual System by : Jonathan Stone
Download or read book Parallel Processing in the Visual System written by Jonathan Stone and published by Springer Science & Business Media. This book was released on 2013-03-08 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the mid-sixties, John Robson and Christina Enroth-Cugell, without realizing what they were doing, set off a virtual revolution in the study of the visual system. They were trying to apply the methods of linear systems analysis (which were already being used to describe the optics of the eye and the psychophysical performance of the human visual system) to the properties of retinal ganglion cells in the cat. Their idea was to stimulate the retina with patterns of stripes and to look at the way that the signals from the center and the antagonistic surround of the respective field of each ganglion cell (first described by Stephen Kuffier) interact to generate the cell's responses. Many of the ganglion cells behaved themselves very nicely and John and Christina got into the habit (they now say) of calling them I (interesting) cells. However. to their annoyance, the majority of neurons they recorded had nasty, nonlinear properties that couldn't be predicted on the basis of simple summ4tion of light within the center and the surround. These uncoop erative ganglion cells, which Enroth-Cugell and Robson at first called D (dull) cells, produced transient bursts of impulses every time the distribution of light falling on the receptive field was changed, even if the total light flux was unaltered.
Book Synopsis Scientific and Technical Aerospace Reports by :
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1992 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Neuronal Operations in the Visual Cortex by : G.A. Orban
Download or read book Neuronal Operations in the Visual Cortex written by G.A. Orban and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: The invitation by the editors of the series "studies of brain function" to contribute a monograph on the visual cortex gives me the opportunity to present in a concentrated manner much of the work I have done on the visual cortical areas of cat and monkey. However, the field of visual cortical physi ology is so active and so diverse that the presentation of only my own work would have given a very incomplete view of visual cortical functioning. Therefore this monograph also reviews most of the studies carried out on the subject in the last two decades. Where possible I have tried not only to describe the cortical machinery but also its possible functional purpose regarding vision. In doing this I have expressed my personal views rather than just reviewing the experimental facts. Much of the work presented in this monograph has been supported by the National Research Council of Belgium and the Research Council of the Catholic University of Leuven. I express my gratitude to them. I have en joyed collaborating in these studies with P. O. Bishop, H. Kato, H. Kennedy, K. P. Hoffmann, H. Maes, J. Duysens, E. Vandenbussche, and H. van der Glas. I am much indebted to all those who have commented on earlier versions of this monograph: J. Allman, H. Barlow, J. BuBier, M. Callens, J. Duysens, O. J. Griisser, P. Heggelund, H. Kennedy, L. C. Orban and L. Palmer.
Book Synopsis Nonlinear Subunit Models of Neuronal Receptive Fields in the Early Visual Pathway by : Amol Gharat
Download or read book Nonlinear Subunit Models of Neuronal Receptive Fields in the Early Visual Pathway written by Amol Gharat and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Our visual system is sensitive to boundaries defined by differences in cues such as luminance (first-order cue), as well as texture, contrast, or motion (second-order cues). Gradients in these cues can be utilized to perform tasks such as figure-ground segregation and 3D shape perception. A significant fraction of neurons in the early visual cortex of cats and monkeys have been shown to be selective to both first- and second-order boundaries. These neurons are thought to be the neural correlate for perceptual encoding of such boundaries. They are selective for the same boundary orientation irrespective of the cue (first- or second-order) that defines it ("form cue-invariance"), which makes these neurons powerful candidates for the task of segmentation. However, the neural circuitry that gives rise to this selectivity for the early stages of visual processing remains unclear. To address this question, I perform neurophysiological recordings at the early stages of the visual pathway in cats, and then build biologically inspired neural circuit models that can account for visual response properties of neurons at subcortical as well as early cortical stages. In Chapter 2, I use multi-electrode recordings to demonstrate the presence of a significant fraction of neurons in cat Area 18 with nonlinear receptive fields like those of subcortical Y-type cells. These neurons have receptive field properties intermediate between subcortical Y cells and cortical orientation selective cue-invariant neurons. These are strong candidates for building cue-invariant orientation-selective neurons. Furthermore I present a novel neural circuit model that pools such Y-like neurons in an unbalanced "push-pull" manner, to generate orientation-selective cue-invariant receptive fields.In Chapter 3, I estimate biologically constrained neural network models of cat LGN receptive fields using recent machine learning methods (deep learning). The receptive fields are modeled as arising from a two-stage convolutional neural network model. The first stage, corresponding to retinal bipolar cell subunits, is modeled as a convolutional filter layer, and the second stage is modeled as a pooling layer. These two layers are separated by an intermediate parametric nonlinearity. I train such a neural network model for each recorded LGN neuron, using its spiking responses to naturalistic texture stimuli. These models are not only better in comparison to the standard linear-nonlinear models at predicting response to arbitrary stimuli, but they also recover biologically interpretable subunit models.In chapter 4, I evaluate the integration of ON- and OFF-pathway inputs by individual neurons in early cortical areas of the cat (Area 17 and Area 18). In this study, I model receptive fields of cortical simple cells as a linear weighted sum of rectified inputs from model ON- and OFF-center LGN afferents, with the weights estimated using a regression framework. The estimated models reveal significant asymmetries in spatiotemporal integration of ON and OFF signals within simple cell receptive fields. These observed asymmetries could provide the neural mechanism for generating cue-invariant receptive fields from Y-pathway inputs.In summary, I put together our knowledge of retinal as well as early cortical processing to show how spatial nonlinearities emerging from the retina could provide an essential basis for cortical visual processing. I further evaluate these neural mechanisms by estimating single neuron receptive field models, using modern system identification methods. Finally I propose, and provide supportive evidence for, a novel neural circuit mechanism that could explain the cue-invariant processing of luminance- and texture-defined boundaries through a common pathway." --
