A graphical, scalable and intuitive method for the placement and the connection of biological cells

Nicolas P. Rougier 1
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : We introduce a graphical method originating from the computer graphics domain that is used for the arbitrary and intuitive placement of cells over a two-dimensional manifold. Using a bitmap image as input, where the color indicates the identity of the different structures and the alpha channel indicates the local cell density, this method guarantees a discrete distribution of cell position respecting the local density function. This method scales to any number of cells, allows to specify several different structures at once with arbitrary shapes and provides a scalable and versatile alternative to the more classical assumption of a uniform non-spatial distribution. Furthermore, several connection schemes can be derived from the paired distances between cells using either an automatic mapping or a user-defined local reference frame, providing new computational properties for the underlying model. The method is illustrated on a discrete homogeneous neural field, on the distribution of cones and rods in the retina and on a coronal view of the basal ganglia.
Type de document :
Pré-publication, Document de travail
Corresponding code at https://github.com/rougier/spatial-computation. 2017
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Contributeur : Nicolas P. Rougier <>
Soumis le : mardi 17 octobre 2017 - 07:40:44
Dernière modification le : jeudi 11 janvier 2018 - 06:24:26


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Nicolas P. Rougier. A graphical, scalable and intuitive method for the placement and the connection of biological cells. Corresponding code at https://github.com/rougier/spatial-computation. 2017. 〈hal-01617732〉



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