{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-graphical-scalable-and-intuitive-method-for","title":"A graphical, scalable and intuitive method for the placement and the connection of biological cells","arxiv_id":"1710.05189","date":"2017-10-14","proceeding":null,"authors":["Nicolas P. Rougier"],"abstract":"We introduce a graphical method originating from the computer graphics domain\nthat is used for the arbitrary and intuitive placement of cells over a\ntwo-dimensional manifold. Using a bitmap image as input, where the color\nindicates the identity of the different structures and the alpha channel\nindicates the local cell density, this method guarantees a discrete\ndistribution of cell position respecting the local density function. This\nmethod scales to any number of cells, allows to specify several different\nstructures at once with arbitrary shapes and provides a scalable and versatile\nalternative to the more classical assumption of a uniform non-spatial\ndistribution. Furthermore, several connection schemes can be derived from the\npaired distances between cells using either an automatic mapping or a\nuser-defined local reference frame, providing new computational properties for\nthe underlying model. The method is illustrated on a discrete homogeneous\nneural field, on the distribution of cones and rods in the retina and on a\ncoronal view of the basal ganglia.","url_abs":"http://arxiv.org/abs/1710.05189v1","url_pdf":"http://arxiv.org/pdf/1710.05189v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-graphical-scalable-and-intuitive-method-for","repo_url":"https://github.com/rougier/spatial-computation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}