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. |
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dc.language.iso |
en |
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dc.publisher |
ELSEVIER IRELAND LTD |
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dc.subject |
Mind |
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dc.subject |
Sensation |
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dc.subject |
Perception |
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dc.subject |
Evolution |
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dc.subject |
Pattern recognition |
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dc.title |
Multidimensional brain activity dictated by winner-take-all mechanisms |
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dc.type |
Article |
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dc.contributor.authorID |
0000-0001-8426-4860 |
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dc.contributor.department |
Univ North Texas, Ctr Nonlinear Sci, |
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dc.contributor.department |
Univ Manitoba, Computat Intelligence Lab, |
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dc.contributor.department |
Univ Manitoba, Dept Elect & Comp Engn |
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dc.contributor.department |
Adiyaman Univ, Dept Math, |
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dc.identifier.endpage |
89 |
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dc.identifier.startpage |
83 |
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dc.identifier.volume |
678 |
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dc.source.title |
NEUROSCIENCE LETTERS |
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