Depth perception in frogs and toads: a study in neural computing
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.
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Monocular and Binocular Cooperation
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accommodation controller accommodation cues adjustment algorithms angular animals Arbib assumption axis behavior binocular cues binocular depth cues binocular disparity binocular input binocular layers binocular matching binocularly computer simulations constraint contralateral convergence coordinate system cue interaction model deadband depth estimate depth information depth map depth perception depth resolution disambiguate distance effect efferent equations error excitation excitatory spread experiments fence FIGURE Fite frogs and toads ghost Grobstein horopter image focus Ingle inhibitory layers internal potential ipsilateral lens accommodation lesioned monocular monocular toads motor neural nucleus isthmi optic flow optic nerve orientation output pathways pattern recognizers pretectum prey catching prey localization model prey selectors prisms projection receptive field region relay layers represent retinal angle retinal position Scalia shown in Fig shows single prey object snapping spatial spread function tectal lobe tectum test scene thalamus threshold Udin vergence visual field visual input visual system