A graphical, scalable and intuitive method for the placement and the connection of biological cells
This provides a scalable and versatile alternative to uniform non-spatial distributions for researchers in computational biology and neuroscience, though it appears incremental as it adapts existing graphical techniques to this domain.
The authors tackled the problem of arbitrary and intuitive placement of biological cells on 2D manifolds by introducing a graphical method from computer graphics, which uses bitmap images to specify cell identity and density, ensuring discrete distributions that respect local density functions and scale to any number of cells.
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.