pulse2percept 0.6.0 documentation¶
By 2020 roughly 20 million people will suffer from retinal diseases such as macular degeneration or retinitis pigmentosa, and a variety of sight restoration technologies are being developed to target these diseases.
Retinal prostheses, now implanted in over 300 patients worldwide, electrically stimulate surviving cells in order to evoke neuronal responses that are interpreted by the brain as visual percepts (‘phosphenes’). However, interactions between the device electronics and the retinal neurophysiology result in perceptual distortions that may severely limit the quality of the generated visual experience:
Built on the NumPy and SciPy stacks, pulse2percept provides an open-source implementation of a number of computational models for state-of-the-art visual prostheses (also known as the ‘bionic eye’), such as ArgusII and AlphaIMS, to provide insight into the visual experience provided by these devices.
Simulations such as the above are likely to be critical for providing realistic estimates of prosthetic vision, thus providing regulatory bodies with guidance into what sort of visual tests are appropriate for evaluating prosthetic performance, and improving current and future technology.
If you use pulse2percept in a scholarly publication, please cite as:
M Beyeler, GM Boynton, I Fine, A Rokem (2017). pulse2percept: A Python-based simulation framework for bionic vision. Proceedings of the 16th Python in Science Conference (SciPy), p.81-88, doi:10.25080/shinma-7f4c6e7-00c.
Installation¶
The latest stable release of pulse2percept can be installed with pip:
pip3 install pulse2percept
In order to get the bleeding-edge version of pulse2percept, use the commands:
git clone https://github.com/pulse2percept/pulse2percept.git
cd pulse2percept
pip3 install -e .
When installing the bleeding-edge version on Windows, note that you will have to install your own C compiler first.
Detailed instructions for different platforms can be found in our Installation Guide.
Where to go from here¶
- Have a look at some code examples from our Example Gallery.
- Familiarize yourself with visual prostheses, electrical stimuli, and our computational models.
- See if your question has already been addressed in the FAQ.
- Request features or report bugs in our Issue Tracker on GitHub.
This work was supported by a grant from the Gordon & Betty Moore Foundation and the Alfred P. Sloan Foundation to the University of Washington eScience Institute.