Adıyaman Üniversitesi Kurumsal Arşivi

Multidimensional brain activity dictated by winner-take-all mechanisms

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dc.contributor.author Tozzi, Arturo
dc.contributor.author Peters, James
dc.date.accessioned 2024-11-11T12:33:47Z
dc.date.available 2024-11-11T12:33:47Z
dc.date.issued 2018
dc.identifier.issn 0304-3940
dc.identifier.uri http://dspace.adiyaman.edu.tr:8080/xmlui/handle/20.500.12414/5398
dc.description.abstract A novel demon-based architecture is introduced to elucidate brain functions such as pattern recognition during human perception and mental interpretation of visual scenes. Starting from the topological concepts of in-variance and persistence, we introduce a Selfridge pandemonium variant of brain activity that takes into account a novel feature, namely, demons that recognize short straight-line segments, curved lines and scene shapes, such as shape interior, density and texture. Low-level representations of objects can be mapped to higher-level views (our mental interpretations): a series of transformations can be gradually applied to a pattern in a visual scene, without affecting its invariant properties. This makes it possible to construct a symbolic multi-dimensional representation of the environment. These representations can be projected continuously to an object that we have seen and continue to see, thanks to the mapping from shapes in our memory to shapes in Euclidean space. Although perceived shapes are 3-dimensional (plus time), the evaluation of shape features (volume, color, contour, closeness, texture, and so on) leads to n-dimensional brain landscapes. Here we discuss the advantages of our parallel, hierarchical model in pattern recognition, computer vision and biological nervous system's evolution. tr
dc.language.iso en tr
dc.publisher ELSEVIER IRELAND LTD tr
dc.subject Mind tr
dc.subject Sensation tr
dc.subject Perception tr
dc.subject Evolution tr
dc.subject Pattern recognition tr
dc.title Multidimensional brain activity dictated by winner-take-all mechanisms tr
dc.type Article tr
dc.contributor.authorID 0000-0001-8426-4860 tr
dc.contributor.department Univ North Texas, Ctr Nonlinear Sci, tr
dc.contributor.department Univ Manitoba, Computat Intelligence Lab, tr
dc.contributor.department Univ Manitoba, Dept Elect & Comp Engn tr
dc.contributor.department Adiyaman Univ, Dept Math, tr
dc.identifier.endpage 89 tr
dc.identifier.startpage 83 tr
dc.identifier.volume 678 tr
dc.source.title NEUROSCIENCE LETTERS tr


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