Michael Beyeler

About

Dr. Michael Beyeler is an Associate Professor of Computer Science and Psychological & Brain Sciences at UC Santa Barbara, where he also serves as the Associate Director of the Center for Virtual Environments and Behavior (ReCVEB). He directs the Bionic Vision Lab, which investigates how neural systems give rise to visual perception and explores methods to restore sight in individuals with incurable blindness. His research combines computational neuroscience, artificial intelligence, and immersive technology, utilizing computational models, neurophysiological methods, and behavioral experiments to understand neural dynamics related to perception and navigation. The lab focuses on the emerging field of bionic vision, developing biophysically grounded models of retinal and cortical stimulation, and designing neuroprosthetic interfaces through immersive virtual reality environments and multimodal neural sensing techniques. Dr. Beyeler's work aims to inform the design of next-generation visual neuroprostheses by collaborating with implant developers and bionic eye recipients, translating scientific insights into real-world applications.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Neuroscience
• Medicine
• Computer vision
• Optometry
• Engineering
• Human–computer interaction
• Biomedical engineering

Selected publications

• A systematic review of extended reality (XR) for understanding and augmenting vision loss

  Journal of Vision · 2023 · 46 citations

  Senior authorCorresponding

  Over the past decade, extended reality (XR) has emerged as an assistive technology not only to augment residual vision of people losing their sight but also to study the rudimentary vision restored to blind people by a visual neuroprosthesis. A defining quality of these XR technologies is their ability to update the stimulus based on the user's eye, head, or body movements. To make the best use of these emerging technologies, it is valuable and timely to understand the state of this research and…

  DOI

• Deep Learning–Based Scene Simplification for Bionic Vision

  2021 · 45 citations

  Senior authorCorresponding

  Retinal degenerative diseases cause profound visual impairment in more than 10 million people worldwide, and retinal prostheses are being developed to restore vision to these individuals. Analogous to cochlear implants, these devices electrically stimulate surviving retinal cells to evoke visual percepts (phosphenes). However, the quality of current prosthetic vision is still rudimentary. Rather than aiming to restore “natural” vision, there is potential merit in borrowing state-of-the-art compu…

  DOI

• A Computational Model of Phosphene Appearance for Epiretinal Prostheses

  2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) · 2021 · 29 citations

  Senior authorCorresponding

  Retinal neuroprostheses are the only FDA-approved treatment option for blinding degenerative diseases. A major outstanding challenge is to develop a computational model that can accurately predict the elicited visual percepts (phosphenes) across a wide range of electrical stimuli. Here we present a phenomenological model that predicts phosphene appearance as a function of stimulus amplitude, frequency, and pulse duration. The model uses a simulated map of nerve fiber bundles in the retina to pro…

  DOI

• Factors affecting two-point discrimination in Argus II patients

  Frontiers in Neuroscience · 2022 · 12 citations

  Senior authorCorresponding

  Two of the main obstacles to the development of epiretinal prosthesis technology are electrodes that require current amplitudes above safety limits to reliably elicit percepts, and a failure to consistently elicit pattern vision. Here, we explored the causes of high current amplitude thresholds and poor spatial resolution within the Argus II epiretinal implant. We measured current amplitude thresholds and two-point discrimination (the ability to determine whether one or two electrodes had been s…

  DOI

• Perceptual learning of prosthetic vision using video game training

  Journal of Vision · 2025-10-07 · 2 citations

  articleOpen access

  A key limitation shared by both electronic and optogenetic sight recovery technologies is that they cause simultaneous rather than complementary firing within on- and off-center cells. Here, using "virtual patients"-sighted individuals viewing distorted input-we examine whether gamified training improves the ability to compensate for distortions in neuronal population coding. We measured perceptual learning using dichoptic input, filtered so that regions of the image that produced on-center resp…

  PublisherOA PDFDOI

Recent grants

• Virtual prototyping for retinal prosthesis patients

  NIH · $633k · 2020–2023

• Virtual prototyping for retinal prosthesis patients

  NIH · $245k · 2018–2020

Frequent coauthors

• Ione Fine

  21 shared

• Ariel Rokem

  University of Washington

  18 shared

• Geoffrey M. Boynton

  University of Washington

  18 shared

• Jeffrey L. Krichmar

  13 shared

• Justin Kasowski

  University of California, Santa Barbara

  13 shared

• Jacob Granley

  University of California, Santa Barbara

  13 shared

• Nikil Dutt

  11 shared

• Reinhard Dummer

  University Hospital of Zurich

  10 shared

Labs

• Bionic Vision LabPI

Education

• Ph.D., Computer Science

  UC Irvine

• B.S., Electrical and Biomedical Engineering

  ETH Zurich

Awards & honors

• K99/R00 (NIH)
• DP2 New Innovator Award (NIH)
• 2024-2025 Harold J. Plous Memorial Award

Similar researchers at University of California, Santa Barbara

• Kenneth S. Kosikh-120
• Jean Carlsonh-48
• Miguel P. Ecksteinh-47