The following model was conducted in a Python 3.7 environment, importing NumPy and Scikit-learn libraries for computational functions, and the MatPlotLib library for visualization.
15–17 All simulations were performed on a Dell Inspiron 14 7490 with 8 gb of RAM and using an Intel i7-10510u processor at 1.80 gHz processing speed. The RPE was modeled as a 300 × 300 dimensional array, wherein each array element represents a cluster of cells, and the array was made circular by excluding array elements farther than 150 units from the center (see
Fig. 1). The size of this array was modeled as being equivalent to 55° of retinal eccentricity, centered on the fovea, as this was the field limit used in the comparative meta-analysis.
8 As a result, each array element or “cell cluster” represents approximately 205 to 260 cells.
18 Each array element was assigned one of two states; either a “1” indicating the cluster of cells was healthy, or “0” indicating the cell cluster was dead. This assignment was represented by either dark or light clusters, respectively, on a diagram to visualize this effect (see
Fig. 1). In the initial condition, each cell cluster was assumed to contain only live cells, which gives the array a homogeneous dark grey color (
Fig. 1, column 1). When a cell dies, the dark grey cluster turns to light grey (
Fig. 1, column 2). Then, an algorithm was implemented to simulate rules that may govern cell death, namely, either a background effect, neighbor effect, or a combination of the two over a course of time steps.