Overview

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.

Full Product Details

Author:   Donald House
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   1989 ed.
Volume:   80
Dimensions:   Width: 17.00cm , Height: 0.80cm , Length: 24.40cm
Weight:   0.276kg
ISBN:  

9780387971575

ISBN 10:   0387971572
Pages:   135
Publication Date:   22 November 1989
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

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.- 2.2.1 The role of depth perception in detour behavior.- 2.2.2 Monocular and binocular depth cues.- 2.3 Anatomy and Physiology.- 2.3.1 The eyes.- 2.3.2 Optic nerve projection sites.- 2.3.3 Nucleus isthmi - a source of binocular input to tectum.- 2.3.4 Binocular units in tectum and thalamus.- 2.3.5 Efferent pathways from tectum and thalamus.- 2.4 Functional Analysis of the Major Visuomotor Centers.- 2.4.1 Retina.- 2.4.2 Functions of thalamus and tectum.- 2.4.3 Evidence for depth perception in thalamus.- 2.4.4 Motor pathways from tectum and thalamus.- 2.4.5 Depth perception dissociated from orientation.- 2.4.6 Depth Maps, Motor activity and tectal retinotopy.- 2.4.7 Summary of the frog/toad visual system.- 2.5 Modeling Assumptions.- 2.6 Conclusions.- 3 Monocular and Binocular Cooperation.- 3.1 Design of the Model.- 3.1.1 Structure.- 3.1.2 Constraints.- 3.1.3 Coordinate systems.- 3.1.4 Computational scheme.- 3.2 Methods.- 3.2.1 Computer simulation.- 3.2.2 Visual input.- 3.3 Results.- 3.3.1 Initial validation.- 3.3.2 Effect of lateral excitatory spread.- 3.3.3 Depth segmentation.- 3.3.4 Effects of lenses and prisms.- 3.3.5 Barrier depth resolution.- 3.3.6 Monocular response.- 3.4 Discussion.- 4 Localization of Prey.- 4.0.1 Background.- 4.0.2 Model overview.- 4.1 Methods.- 4.1.1 Computer simulation.- 4.1.2 Mathematical description for the simulation.- 4.1.3 Graphical displays.- 4.1.4 Depth estimate categories.- 4.1.5 Visual input.- 4.2 Results.- 4.2.1 Experiments with single-prey stimuli.- 4.2.2 Experiments with multiple-prey stimuli.- 4.2.3 The effect of lenses on two-prey experiments.- 4.3 Discussion.- 4.3.1 A possible neural realization of the model.- 4.3.2 Suitability of the nucleus isthmi as a tecto-tectal relay for depth perception.- 4.3.3 Evaluation of the model.- 5 Towards a Complete Model.- 5.1 The Cue Interaction and Prey Localization Models.- 5.1.1 Unities.- 5.1.2 Diversities.- 5.1.3 A synthesis.- 5.2 An Extended Model of Accommodation Control.- 5.3 Experimental Verification of the Models' 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.

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