Rather than predicting perceptual distortions, one needs to solve the inverse problem: What is the best stimulus to generate a desired visual percept?
Galen Pogoncheff joined the Bionic Vision Lab in 2022 as a PhD student in the Computer Science department. Driven to improve the processing of visual stimuli for bionic vision devices, he aims to advance techniques in computer vision using mechanisms inspired by neural processing.
Prior to joining the lab, Galen obtained his B.S. and M.S. in Computer Science from the University of Colorado and subsequently led the research and development of machine learning models for wearable electrophysiology devices at a local startup.
MS in Computer Science, 2020
University of Colorado, Boulder
BS in Computer Science, 2018
University of Colorado, Boulder
Rather than predicting perceptual distortions, one needs to solve the inverse problem: What is the best stimulus to generate a desired visual percept?
What do visual prosthesis users see, and why? Clinical studies have shown that the vision provided by current devices differs substantially from normal sight.
We systematically incorporated neuroscience-derived architectural components into CNNs to identify a set of mechanisms and architectures that comprehensively explain neural activity in V1.
Galen Pogoncheff, Jacob Granley, Michael Beyeler arXiv:2305.11275
We present explainable artificial intelligence (XAI) models fit on a large longitudinal dataset that can predict perceptual thresholds on individual Argus II electrodes over time.
Galen Pogoncheff, Zuying Hu, Ariel Rokem, Michael Beyeler medRxiv:23285633