Vision+Cognition Group

The VisCog Group at the University of Washington is headed by Geoffrey Boynton, Ione Fine, and Scott Murray.

UW Toolbox

A Matlab toolbox for vision research

The UW toolbox is a suite of Matlab functions used in vision research, created by Geoff Boynton and Ione Fine, and maintained by Kelly Chang.
GitHub repo


A Python-based simulation framework for bionic vision

Retinal dystrophies such as retinitis pigmentosa and macular degeneration result in profound visual impairment in more than 10 million people worldwide. One treatment approach, visual neuroprostheses, aims to restore vision by electrically stimulating surviving cells in the retina, analogous to cochlear implants.
To better understand the outcomes of this technology, we developed pulse2percept, an open-source Python implementation of a computational model that predicts the perceptual experience of retinal prosthesis patients across a wide range of implant configurations.
GitHub repo Documentation Beyeler et al. (2017)


Beyeler et al. (2018): A model of ganglion axon pathways accounts for percepts elicited by retinal implants

Current retinal implant users report seeing distorted and often elongated shapes rather than small focal spots of light that match the shape of the implant electrodes.
Here we show that the perceptual experience of retinal implant users can be accurately predicted using a computational model that simulates each individual patient’s retinal ganglion axon pathways.
This opens up the possibility for future devices that incorporate stimulation strategies tailored to each individual patient’s retina.
Preprint Code Data

Shen et al. (2018): Women in high-profile journals

Our analysis of primary research papers in 15 prestigious multidisciplinary and neuroscience journals in the MEDLINE database indicates that the proportion of female authors in these journals has been consistently low over the past 13 years.
Publication in distinguished journals advances careers, so this under-representation negatively affects the careers of thousands of female scientists.
Shen et al. (2018) Preprint Code