2017/10/14 by Nicolas P. Rougier
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Neuroscience · #Advanced Fluorescence Microscopy Techniques #Algorithm #Bitmap #Cell Image Analysis Techniques #Combinatorics #Computer graphics #Computer graphics (images) #Computer science #Distribution (mathematics) #Domain (mathematical analysis) #Graphics #Manifold (fluid mechanics) #Mathematical analysis #Mathematics #Neural dynamics and brain function #Scalability #Topology (electrical circuits) #cs.GR #cs.NE #q-bio.NC
paper · pdf · doi:10.3389/fninf.2018.00012
Corresponding code at https://github.com/rougier/spatial-computation
arxiv created 2017/10/14 · openalex publication_date 2018/03/20 · arxiv updated 2018/03/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
We introduce a graphical method originating from the computer graphics domain that is used for the arbitrary placement of cells over a two-dimensional manifold. Using a bitmap image whose luminance provides cell density, this method guarantees a discrete distribution of the positions of the cells respecting the local density. This method scales to any number of cells, allows one to specify arbitrary enclosing shapes and provides a scalable and versatile alternative to the more classical assumption of a uniform spatial distribution. The method is illustrated on a discrete homogeneous neural field, on the distribution of cones and rods in the retina and on the neural density of a flattened piece of cortex.