A normal human retina contains several classes of photosensitive cell—rods for low-light vision, three types of cones for daylight vision, and the intrinsically photosensitive retinal ganglion cells (ipRGCs) expressing melanopsin for controlling non-image-forming functions (e.g., pupil size, circadian rhythms). The spectral sensitivities of the photoreceptors overlap significantly, meaning most lights will stimulate all photoreceptors, but to varying degrees. The method of silent substitution (Estévez & Spekreijse, 1982, Vision Research, 22[6], 681-691) provides a principled basis for stimulating individual photoreceptor classes selectively, which is useful in research and clinical settings. The main hardware requirement for silent substitution is a spectrally calibrated light stimulation system with at least as many primaries as there are photoreceptors under consideration. Device settings that will produce lights to selectively stimulate the photoreceptor(s) of interest can be found using a variety of analytic and algorithmic approaches. Here we present PySilSub, a novel Python package for silent substitution featuring object-oriented support for individual colorimetric observer models, multi-primary stimulation devices, and solving silent substitution problems with linear algebra and constrained numerical optimisation. The software is registered with the Python Package Index (pip install pysilsub) and includes example data sets from various multi-primary systems. We hope that PySilSub will further encourage the application of silent substitution in research and clinical settings.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)