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|a 9783319655918
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|a Petitot, Jean
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|a Elements of Neurogeometry
|h Elektronische Ressource
|b Functional Architectures of Vision
|c by Jean Petitot
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250 |
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|a 1st ed. 2017
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260 |
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|a Cham
|b Springer International Publishing
|c 2017, 2017
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300 |
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|a XV, 379 p. 257 illus., 186 illus. in color
|b online resource
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505 |
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|a Preface -- Introdcution -- Receptive Fields and Profiles, and Wavelet Analysis -- Pinwheels of V1 Horizontal Connections and Contact Structure -- Transition to Volume II -- References
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653 |
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|a Mathematical and Computational Biology
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653 |
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|a Mathematical Models of Cognitive Processes and Neural Networks
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653 |
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|a Biomathematics
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653 |
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|a Neural networks (Computer science)
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653 |
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|a Geometry
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041 |
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|a eng
|2 ISO 639-2
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989 |
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|b Springer
|a Springer eBooks 2005-
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490 |
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|a Lecture Notes in Morphogenesis
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|a 10.1007/978-3-319-65591-8
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|u https://doi.org/10.1007/978-3-319-65591-8?nosfx=y
|x Verlag
|3 Volltext
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|a 570.285
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|a This book describes several mathematical models of the primary visual cortex, referring them to a vast ensemble of experimental data and putting forward an original geometrical model for its functional architecture, that is, the highly specific organization of its neural connections. The book spells out the geometrical algorithms implemented by this functional architecture, or put another way, the “neurogeometry” immanent in visual perception. Focusing on the neural origins of our spatial representations, it demonstrates three things: firstly, the way the visual neurons filter the optical signal is closely related to a wavelet analysis; secondly, the contact structure of the 1-jets of the curves in the plane (the retinal plane here) is implemented by the cortical functional architecture; and lastly, the visual algorithms for integrating contours from what may be rather incomplete sensory data can be modelled by the sub-Riemannian geometry associated with this contact structure. As such, it provides readers with the first systematic interpretation of a number of important neurophysiological observations in a well-defined mathematical framework. The book’s neuromathematical exploration appeals to graduate students and researchers in integrative-functional-cognitive neuroscience with a good mathematical background, as well as those in applied mathematics with an interest in neurophysiology
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