A Python-based simulation framework for bionic vision

Retinal degenerative diseases such as retinitis pigmentosa and macular degeneration result in profound visual impairment in more than 10 million people worldwide, and a variety of sight restoration technologies are being developed to target these diseases.

Retinal prostheses, now implanted in over 500 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:


(left: input stimulus, right: predicted percept)

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, BVA24, and PRIMA, 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.


Once you have Python 3 and pip, the stable release of pulse2percept can be installed with pip:

pip3 install pulse2percept

The bleeding-edge version of pulse2percept can be installed via:

pip3 install git+https://github.com/pulse2percept/pulse2percept

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.


pulse2percept 0.6 was the last version to support Python <= 3.5. pulse2percept 0.7+ requires Python 3.6+.

pulse2percept requires:

  1. Python (>= 3.6)
  2. Cython (>= 0.28)
  3. NumPy (>= 1.9)
  4. SciPy (>= 1.0.1)
  5. ScikitImage (>=0.14)
  6. Matplotlib (>= 3.0.2)
  7. ImageIO FFMPEG (>=0.4)
  8. JobLib (>= 0.11)

Optional packages:

  1. Pandas for loading datasets in the datasets module.
  2. Scikit Learn for machine learning functionality in the model_selection module.
  3. Dask for parallel processing (a joblib alternative). Use conda to install.
  4. Pytest to run the test suite.
  5. OpenMP for parallel processing support.

All required packages are listed in requirements.txt in the root directory of the git repository, and can be installed with the following command:

git clone https://github.com/pulse2percept/pulse2percept.git
cd pulse2percept
pip3 install -r requirements.txt

All packages required for development (including all optional packages) are listed in requirements-dev.txt and can be installed via:

pip3 install -r requirements-dev.txt