This book provides a practical guide and resource about the current state of the field of retinal computation.
Author: Greg Schwartz
Publisher: Academic Press
Retinal Computation summarizes current progress in defining the computations performed by the retina, also including the synaptic and circuit mechanisms by which they are implemented. Each chapter focuses on a single retinal computation that includes the definition of the computation and its neuroethological purpose, along with the available information on its known and unknown neuronal mechanisms. All chapters contain end-of-chapter questions associated with a landmark paper, as well as programming exercises. This book is written for advanced graduate students, researchers and ophthalmologists interested in vision science or computational neuroscience of sensory systems. While the typical textbook's description of the retina is akin to a biological video camera, the real retina is actually the world’s most complex image processing machine. As part of the central nervous system, the retina converts patterns of light at the input into a rich palette of representations at the output. The parallel streams of information in the optic nerve encode features like color, contrast, orientation of edges, and direction of motion. Image processing in the retina is undeniably complex, but as one of the most accessible parts of the central nervous system, the tools to study retinal circuits with unprecedented precision are up to the task. This book provides a practical guide and resource about the current state of the field of retinal computation. Provides a practical guide on the field of retinal computation Summarizes and clearly explains important topics such as luminance, contrast, spatial features, motion and other computations Contains discussion questions, a landmark paper, and programming exercises within each chapter
Author: International Workshop on Artificial Neural Networks$ (1993 : Sitges, Espagne)Publish On: 1993-05-27
The simulation of the retinal filtering preceded by a variable sampling is not obvious : the retinal computation presented in section 2 was adapted to input data on a squared mesh ( Fig . 6a ) . The introduction of a random disorder is ...
Author: International Workshop on Artificial Neural Networks$ (1993 : Sitges, Espagne)
Publisher: Springer Science & Business Media
Neural computation arises from the capacity of nervous tissue to process information and accumulate knowledge in an intelligent manner. Conventional computational machines have encountered enormous difficulties in duplicatingsuch functionalities. This has given rise to the development of Artificial Neural Networks where computation is distributed over a great number of local processing elements with a high degree of connectivityand in which external programming is replaced with supervised and unsupervised learning. The papers presented in this volume are carefully reviewed versions of the talks delivered at the International Workshop on Artificial Neural Networks (IWANN '93) organized by the Universities of Catalonia and the Spanish Open University at Madrid and held at Barcelona, Spain, in June 1993. The 111 papers are organized in seven sections: biological perspectives, mathematical models, learning, self-organizing networks, neural software, hardware implementation, and applications (in five subsections: signal processing and pattern recognition, communications, artificial vision, control and robotics, and other applications).
the center of the retina and slightly displaced from the optical axis of the eye ball, contains the highest concentration of photoreceptor cones. There are widely varying optical characteristics for each component of the retina.
Author: Emanuele Trucco
Publisher: Academic Press
Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more. Provides a unique, well-structured and integrated overview of retinal image analysis Gives insights into future areas, such as large-scale screening programs, precision medicine, and computer-assisted eye care Includes plans and aspirations of companies and professional bodies
Adaptation provides a ubiquitous strategy for neural circuits to encode their inputs using their limited dynamic range within the variety of sensory environments that they encounter.
Author: David B. Kastner
Adaptation provides a ubiquitous strategy for neural circuits to encode their inputs using their limited dynamic range within the variety of sensory environments that they encounter. However, because of the inherent timescale necessary to optimize the response properties of a cell to its environment, any form of adaptive plasticity can cause a neuron to fail to encode the stimulus when the environment changes. Many ganglion cells, the output neurons of the retina, adapt so as to lower their sensitivity in an environment of high contrast, but if the contrast subsequently decreases the cell will fall below threshold and fail to signal. I have found a distinct form of plasticity within the retina that acts in coordination with the process of adaptation. Cells using this new form of plasticity elevate their sensitivity after a transition to low contrast. This process, called sensitization, occurs in retinas from multiple species. Multielectrode recordings from sensitizing and adapting cells indicate that both populations encode the same visual signals. The complementary action of the two populations helps the retina encode its input over a broader range of signals and environmental changes, with one population continuing to respond when the other fails. The threshold placement of these two cell types further enhances their coordination because sensitizing cells maintain lower thresholds, while adapting cells maintain higher thresholds. Using a theoretical model, I was able to show that this behavior maximized the amount of information that the two populations can provide about their input. I have further studied the spatiotemporal region that controlled the sensitivity of a cell--the adaptive field. Just as retinal circuitry uses excitation and inhibition to form biphasic center-surround receptive fields, the retina can also use adaptation and sensitization to form biphasic adaptive fields in both the spatial and temporal domains. Since visual statistics are correlated across time and space, center-surround biphasic receptive fields more efficiently encode the input by subtracting a prediction of the stimulus so as to just encode the deviation from that prediction. Biphasic adaptive fields appear to perform an opposite function, transmitting a prediction of the stimulus at the transition of a stimulus environment to weaker signals. This assists in the encoding of an uncertain environment by storing features of a predictable input. A model indicates that sensitization within the adaptive field can be produced by adapting inhibition, a form of plasticity whose function was previously unknown. Using pharmacology, I confirmed this prediction, showing that GABAergic inhibition is necessary for sensitization. Using simultaneous intracellular recording from inhibitory amacrine cells and multielectrode recording from ganglion cells, I show that transmission from a single amacrine cell is sucient to cause sensitization. Using a novel approach to analyze a circuit, I quantitatively describe the changes in amacrine cell transmission that underlie sensitization thus elucidating how the retina performs this sophisticated computation.
Compensation of Retinal Degenerations * Rolf Eckmiller University of Bonn, Department of Computer Science VI, D-53117 Bonn, F.R.Germany Abstract. The development of retina implants for blind humans suffering from various retinal ...
Author: Olsson Bjorn
Publisher: World Scientific
This volume contains papers presented at the BCEC97 conference, held in Skövde, Sweden, in September 1997. The conference brought together researchers from biology and computer science to discuss the use of computational techniques in biology, as well as the use of biological metaphors in computing. Examples of the work presented in these papers include computer simulations of embryogenesis; algorithms for protein folding prediction; problem solving using DNA computation; neural-network learning in retina implants; and optimisation algorithms inspired by natural evolution.
23–28 (2015) 2. Dash, J., Bhoi, N.: A survey on blood vessel detection methodologies in retinal images. In: Proceedings of 2015 International Conference on Computational Intelligence and Networks (CINE), pp. 166–171 (2015) 3.
Author: Federico Rossi
This book constitutes the revised selected papers of the 10th Italian Workshop on Advances in Artificial Life, Evolutionary Computation and Systems Chemistry, WIVACE 2015, held at Bari, Italy, in September 2015. The 18 papers presented have been thoroughly reviewed and selected from 45 submissions. They cover the following topics: evolutionary computation, bioinspired algorithms, genetic algorithms, bioinformatics and computational biology, modeling and simulation of artificial and biological systems, complex systems, synthetic and systems biology, systems chemistry.
Author: Jose Manuel Ferrandez VicentePublish On: 2013-06-03
5th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2013, Mallorca, ... 872–873 (1952) Ditchburn, R.W., Fender, D.H., Mayne, S.: Vision with controlled movements of the retinal image.
Author: Jose Manuel Ferrandez Vicente
The two volume-set, LNCS 7930 and LNCS 7931, constitutes the refereed proceedings of the 5th International Work-Conference on the Interplay between Natural and Artificial Computation, IWINAC 2013, held in Mallorca, Spain, in June 2013. The 92 revised full papers presented in LNCS 7930 and LNCS 7931 were carefully reviewed and selected from numerous submissions. The first part, LNCS 7930, entitled "Natural and Artificial Models in Computation and Biology”, includes all the contributions mainly related to the methodological, conceptual, formal, and experimental developments in the fields of neurophysiology and cognitive science. The second part, LNCS 7931, entitled “Natural and Artificial Computation in Engineering and Medical Applications”, contains the papers related to bioinspired programming strategies and all the contributions related to the computational solutions to engineering problems in different application domains, specially Health applications, including the CYTED “Artificial and Natural Computation for Health” (CANS) research network papers. In addition, this two volume-set reflects six interesting areas: cognitive robotics; natural computing; wetware computation; quality of life technologies; biomedical and industrial perception applications; and Web intelligence and neuroscience.
... flickering light requires complex retinal computation, and thus ERG measures are an excellent test of retinal circuit fidelity. Critical flicker frequency (CFF) is the frequency at which the retinal response is no longer modulated.
Author: John D. Ash
The blinding diseases of inherited retinal degenerations have no treatments, and age-related macular degeneration has no cures, despite the fact that it is an epidemic among the elderly, with 1 in 3-4 affected by the age of 70. The RD Symposium will focus on the exciting new developments aimed at understanding these diseases and providing therapies for them. Since most major scientists in the field of retinal degenerations attend the biennial RD Symposia, they are known by most as the “best” and “most important” meetings in the field. The volume will present representative state-of-the-art research in almost all areas of retinal degenerations, ranging from cytopathologic, physiologic, diagnostic and clinical aspects; animal models; mechanisms of cell death; candidate genes, cloning, mapping and other aspects of molecular genetics; and developing potential therapeutic measures such as gene therapy and neuroprotective agents for potential pharmaceutical therapy. While advances in these areas of retinal degenerations will be described, there will be many new topics that either were in their infancy or did not exist at the time of the last RD Symposium, RD2014. These include the role of inflammation and immunity, as well as other basic mechanisms, in age-related macular degeneration, several new aspects of gene therapy, and revolutionary new imaging and functional testing that will have a huge impact on the diagnosis and following the course of retinal degenerations, as well as to provide new quantitative endpoints for clinical trials. The retina is an approachable part of the central nervous system (CNS), and there is a major interest in neuroprotective and gene therapy for CNS diseases and neurodegenerations, in general. It should be noted that with successful and exciting initial clinical trials in neuroprotective and gene therapy, including the restoration of sight in blind children, the retinal degeneration therapies are leading the way towards new therapeutic measures for neurodegenerations of the CNS. Many of the successes recently reported in these areas of retinal degeneration sprang from collaborations established at previous RD Symposia, and many of those will be reported at the RD2018 meeting and included in the proposed volume. We anticipate the excitement of those working in the field and those afflicted with retinal degenerations will be reflected in the volume.
Fast and slow contrast adaptation in retinal circuitry. Neuron 36, 909-919. Baccus, S. A., Olveczky, B. P., Manu, M., and Meister, M. (2008). A retinal circuit that computes object motion. J Neurosci 28, 6807-6817.
Publisher: Stanford University
Neurons have a limited dynamic range. To more efficiently encode the large range of natural inputs, neural circuits adapt by dynamically changing their output range as a function of the input statistics. Variance adaptation provides an informative example of this process, whereby neurons change their response characteristics as a function of variance of their input. When their input distribution changes, sensory systems shift and scale their response curves to efficiently cover the new range of input values and they focus on different segments of the frequency spectrum, for example by choosing to average out the noise in a low signal-to-noise ratio environment by low-pass filtering their input and sacrificing resolution. In multiple sensory systems, adaptation to the variance of a sensory input changes the sensitivity, kinetics and average response over timescales ranging from
Mosaicing the Retinal Fundus Images: A Robust Registration Technique Based Approach Xinge You2,3, Bin Fang1,3, and Yuan Yan Tang2,3 1Center for Intelligent Computation of Information , Chongqing University, China 2 Faculty of ...
Author: Lipo Wang
This book and its sister volumes, i.e., LNCS vols. 3610, 3611, and 3612, are the proceedings of the 1st International Conference on Natural Computation (ICNC 2005), jointly held with the 2nd International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2005, LNAI vols. 3613 and 3614) from 27 to 29 August 2005 in Changsha, Hunan, China.