pulse2percept: A Python-based simulation framework for bionic vision
By 2020 roughly 200 million people will suffer from retinal diseases such as macular degeneration or retinitis pigmentosa. Consequently, a variety of retinal sight restoration procedures are being developed to target these diseases. Electronic prostheses (currently being implanted in patients) directly stimulate remaining retinal cells using electrical current, analogous to a cochlear implant. Optogenetic prostheses (soon to be implanted in human) use optogenetic proteins to make remaining retinal cells responsive to light, then use light diodes (natural illumination is inadequate) implanted in the eye to stimulate these light sensitive cells.
However, these devices do not restore anything resembling natural vision: Interactions between the electronics and the underlying neurophysiology result in significant distortions of the perceptual experience.
We have developed a computer model that has the goal of predicting the perceptual experience of retinal prosthesis patients. The model was developed using a variety of patient data describing the brightness and shape of phosphenes elicited by stimulating single electrodes, and validated against an independent set of behavioral measures examining spatiotemporal interactions across multiple electrodes.
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