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AuthorHouse, Donald. author
TitleDepth Perception in Frogs and Toads [electronic resource] : A Study in Neural Computing / by Donald House
ImprintNew York, NY : Springer US, 1989
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Descript VII, 135 p. online resource


Depth Perception in Frogs and Toads provides a comprehensive exploration of the phenomenon of depth perception in frogs and toads, as seen from a neuro-computational point of view. Perhaps the most important feature of the book is the development and presentation of two neurally realizable depth perception algorithms that utilize both monocular and binocular depth cues in a cooperative fashion. One of these algorithms is specialized for computation of depth maps for navigation, and the other for the selection and localization of a single prey for prey catching. The book is also unique in that it thoroughly reviews the known neuroanatomical, neurophysiological and behavioral data, and then synthesizes, organizes and interprets that information to explain a complex sensory-motor task. The book will be of special interest to that segment of the neural computing community interested in understanding natural neurocomputational structures, particularly to those working in perception and sensory-motor coordination. It will also be of interest to neuroscientists interested in exploring the complex interactions between the neural substrates that underly perception and behavior


1 Introduction -- 2 Modeling Depth Perception in Frogs and Toads -- 2.1 Previous Models of Depth Perception -- 2.2 Depth Perception in Frogs and Toads -- 2.3 Anatomy and Physiology -- 2.4 Functional Analysis of the Major Visuomotor Centers -- 2.5 Modeling Assumptions -- 2.6 Conclusions -- 3 Monocular and Binocular Cooperation -- 3.1 Design of the Model -- 3.2 Methods -- 3.3 Results -- 3.4 Discussion -- 4 Localization of Prey -- 4.0.1 Background -- 4.0.2 Model overview -- 4.1 Methods -- 4.2 Results -- 4.3 Discussion -- 5 Towards a Complete Model -- 5.1 The Cue Interaction and Prey Localization Models -- 5.2 An Extended Model of Accommodation Control -- 5.3 Experimental Verification of the Modelsโ{128}{153} Depth Scale -- 5.4 Discussion -- 6 Conclusions -- 6.1 The Models and their Contributions -- 6.2 Suggestions for Animal Experiments -- 6.3 Suggestions for Robotic Algorithms -- A Modeling and Simulation Details -- A.1 Representation of a Neural Unit -- A.2 Representation of a Neural Layer -- A.3 Numerical Methods -- A.4 The Cue Interaction Model -- A.5 The Prey Localization Model -- B Simulation Optics -- B.1 Optical Geometry -- B.2 Representation of Prisms -- B.3 Retinal Projections -- B.4 Disparity Input Planes -- B.5 Accommodation Input Planes -- B.6 Representation of Lenses -- B.7 Conversion from Internal to External Coordinates -- B.8 Nominal Parameter Settings

Medicine Neurosciences Artificial intelligence Biomathematics Biomedicine Neurosciences Mathematical and Computational Biology Artificial Intelligence (incl. Robotics)


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