Learning orientations: a discrete geometry model
| Autor: | Yuri Dabaghian |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Theoretical computer science
Spatial structure Orientation (computer vision) Computer science Applied Mathematics Discrete geometry Mammalian brain ENCODE Affine geometry Computational Mathematics FOS: Biological sciences Quantitative Biology - Neurons and Cognition Algebraic topology (object) Neurons and Cognition (q-bio.NC) Spatial maps Geometry and Topology |
| Zdroj: | Journal of Applied and Computational Topology. 6:193-220 |
| ISSN: | 2367-1734 2367-1726 |
| DOI: | 10.1007/s41468-021-00084-0 |
| Popis: | In the mammalian brain, many neuronal ensembles are involved in representing spatial structure of the environment. In particular, there exist cells that encode the animal's location and cells that encode head direction. A number of studies have addressed properties of the spatial maps produced by these two populations of neurons, mainly by establishing correlations between their spiking parameters and geometric characteristics of the animal's environments. The question remains however, how the brain may intrinsically combine the direction and the location information into a unified spatial framework that enables animals' orientation. Below we propose a model of such a framework, using ideas and constructs from algebraic topology and synthetic affine geometry. 17 pages, 5 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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