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Titlebook: ICANN ’94; Proceedings of the I Maria Marinaro,Pietro G. Morasso Conference proceedings 1994 Springer-Verlag London Limited 1994 artificial

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发表于 2025-3-21 18:55:58 | 显示全部楼层 |阅读模式
书目名称ICANN ’94
副标题Proceedings of the I
编辑Maria Marinaro,Pietro G. Morasso
视频video
图书封面Titlebook: ICANN ’94; Proceedings of the I Maria Marinaro,Pietro G. Morasso Conference proceedings 1994 Springer-Verlag London Limited 1994 artificial
描述From its early beginnings in the fifties and sixties the field of neural networks has been steadily growing. The first wave was driven by a handful of pioneers who first discovered analogies between machines and biological systems in communication, control and computing. Technological constraints held back research considerably, but gradually computers have become less expensive and more accessible and software tools inceasingly more powerful. Mathematical techniques, developed by computer-aware people, have steadily accumulated and the second wave has begun. Researchers from such diverse areas as psychology, mathematics, physics, neuroscience and engineering now work together in the neural networking field.
出版日期Conference proceedings 1994
关键词artificial intelligence; artificial neural network; control; fuzzy logic; fuzzy set; fuzzy sets; fuzzy sys
版次1
doihttps://doi.org/10.1007/978-1-4471-2097-1
isbn_softcover978-3-540-19887-1
isbn_ebook978-1-4471-2097-1
copyrightSpringer-Verlag London Limited 1994
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A Learning Rule for Self-organization of The Velocity Selectivity of Directionally Selective Cellsselectivity of directionally selective cells. The proposed learning rule is simple and plausible in the actual nervous system in that it is described only with local information. Numerical simulation results showed that the basic module learned self-organizingly to acquire the selectivity for velocity of an input stimulus.
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Why Bright Kanizsa Squares Look Closer: Consistency of Segmentations and Surfaces in 3-D Visiont are biological vision processes from those of traditional machine vision algorithms. The present article sketches an explanation of this percept as part of a larger theory of biological vision that develops a solution of the classical figure-ground problem (Grossberg, 1993, 1994).
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Motion analysis with recurrent neural netsthe input sequence relevant to prediction and recognition, (b) the specification of a function that combines the current memory and the current input in order to form a new temporal context (Mozer, 1993), (c) to identify and learn the regularities from the temporal sequence.
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Hybrid Methods for Robust Irradiance Analysis and 3-D Shape Reconstruction from Imagesirst order differential structure of the image expressed as the isoluminance direction and gradient magnitude. The (Grossberg and Mingolla, 1985) model of low level visual processes uses input sensors approximately sensitive to brightness gradient, providing a map of segments oriented along constant brightness lines.
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Conference proceedings 1994 pioneers who first discovered analogies between machines and biological systems in communication, control and computing. Technological constraints held back research considerably, but gradually computers have become less expensive and more accessible and software tools inceasingly more powerful. Ma
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Top-Down Interference in Visual Perceptionhe bottom-up subliminal visual stimuli that we already proved to interfere on the alternance mechanism. In both cases the will interference is effective. The possible non linear interactions between top-down and bottom-up influences are discussed.
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How To Use Non-Visual Information for Optic Flow Processing in Monkey Visual Cortical Area MSTdhophysics of human heading detection from optic flow [6] [7]. However, human heading detection has been shown to sometimes depend on non-visual eye movement information [11] [13] [14], and area MSTd has been found to contain extraretinal eye movement [8] as well as eye position [1] information.
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