Towards a Smart Bionic Eye

28 February 2024

The National Library of Medicine at the National Institutes of Health (NIH) sent a film crew to UCSB to document the work we do as part of our NIH DP2 New Innovator Award.

3 papers accepted at NeurIPS ‘23

21 September 2023

Our latest work achieves state-of-the-art predictions of primary visual cortex (V1) activity in mouse and monkey, and details a new way to optimize stimulation protocols for visual prostheses.

Towards a Smart Bionic Eye

4 October 2022

Prof. Beyeler receives $1.5 million NIH Director’s New Innovator award to enable an artificial intelligence (AI)-powered visual prosthesis.

A hybrid neural autoencoder to enhance sensory neuroprostheses

21 June 2022

Our approach to end-to-end optimization of bionic vision was featured in a news article by TechXplore.

Are we witnessing the dawn of post-theory science?

9 January 2022

Prof. Beyeler was mentioned in a recent article by The Guardian that discusses recent advances in machine learning and what data-driven discovery means for the classic methodology of hypothesizing, predicting, and testing.

Illustration: © PM Images/Getty

PCMag: Building the bionic eye… with car tech?

23 February 2021

Interview with Prof. Beyeler: Instead of focusing on one day restoring natural vision, we may be better off thinking about how to create useful artificial vision now.

Illustration: © Yuichiro Chino/Getty

All news articles

More media coverage can be found in our news archive.

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Who We Are

We are an interdisciplinary group interested in exploring the mysteries of human, animal, and artificial vision. Our passion lies in unraveling the science behind bionic technologies that may one day restore useful vision to people living with incurable blindness.

At the heart of our lab is a diverse team that integrates computer science and engineering with neuroscience and psychology. What unites us is a shared fascination with the intricacies of vision and its potential public health applications. However, we are not just about algorithms and data; our research projects range from trying to understand perception in individuals with visual impairments to crafting biophysical models of brain activity and engaging in the transformative world of virtual and augmented reality to create novel visual accessibility tools.

What sets our lab apart is our connection to the community of implant developers and bionic eye recipients. We don't just theorize; we are committed to transforming our ideas into practical solutions that are rigorously tested across different bionic eye technologies. Our goal is to enhance not just scientific understanding, but to foster a greater sense of independence in the lives of those with visual impairments.

Brain Sciences

Vision Sciences

Brain-Computer Devices

Recent Publications

We present a series of analyses on the shared representations between evoked neural activity in the primary visual cortex of a blind human with an intracortical visual prosthesis, and latent visual representations computed in deep neural networks.

We introduce two computational models designed to accurately predict phosphene fading and persistence under varying stimulus conditions, cross-validated on behavioral data reported by nine users of the Argus II Retinal Prosthesis System.

We retrospectively analyzed phosphene shape data collected form three Argus II patients to investigate which neuroanatomical and stimulus parameters predict paired-phosphene appearance and whether phospehenes add up linearly.

Our interview study found a significant gap between researcher expectations and implantee experiences with visual prostheses, underscoring the importance of focusing future research on usability and real-world application.

We systematically incorporated neuroscience-derived architectural components into CNNs to identify a set of mechanisms and architectures that comprehensively explain neural activity in V1.

We propose a personalized stimulus encoding strategy that combines state-of-the-art deep stimulus encoding with preferential Bayesian optimization.

We introduce a multimodal recurrent neural network that integrates gaze-contingent visual input with behavioral and temporal dynamics to explain V1 activity in freely moving mice.

Award Winning Papers

We propose a perceptual stimulus encoder based on convolutional neural networks that is trained in an end-to-end fashion to predict the electrode activation patterns required to produce a desired visual percept.

We combined deep learning-based scene simplification strategies with a psychophysically validated computational model of the retina to generate realistic predictions of simulated prosthetic vision.

In the Spotlight

We present a series of analyses on the shared representations between evoked neural activity in the primary visual cortex of a blind human with an intracortical visual prosthesis, and latent visual representations computed in deep neural networks.

We present a systematic literature review of 227 publications from 106 different venues assessing the potential of XR technology to further visual accessibility.

We developed a spiking neural network model that showed MSTd-like response properties can emerge from evolving spike-timing dependent plasticity with homeostatic synaptic scaling (STDP-H) parameters of the connections between area MT and MSTd.

In the News

What is the required stimulus to produce a desired percept? Here we frame this as an end-to-end optimization problem, where a deep neural network encoder is trained to invert a known, fixed forward model that approximates the underlying biological system.

We optimize electrode arrangement of epiretinal implants to maximize visual subfield coverage.

We combined deep learning-based scene simplification strategies with a psychophysically validated computational model of the retina to generate realistic predictions of simulated prosthetic vision.

Gallery

001 prof

001 prof

002 byron johnson presenting at vss

002 byron johnson presenting at vss

003 argus ii user drawing a perceived phosphene on a touchscreen in front of him

003 argus ii user drawing a perceived phosphene on a touchscreen in front of him

004 group picture of the bvl team at the eye and the chip 2023

004 group picture of the bvl team at the eye and the chip 2023

005 dr justin kasowski getting hooded at the 2023 commencement ceremony

005 dr justin kasowski getting hooded at the 2023 commencement ceremony

006 prof

006 prof

007 prof

007 prof

008 blind participant using a smartphone app to read the label on a jar of mayonnaise

008 blind participant using a smartphone app to read the label on a jar of mayonnaise

009 jacob granley in front of his poster at neur IP s 2022

009 jacob granley in front of his poster at neur IP s 2022

010 justin kasowski presenting his work on simple xr at vss 2023

010 justin kasowski presenting his work on simple xr at vss 2023

011 galen pogoncheff using the bionic vision xr simulator

011 galen pogoncheff using the bionic vision xr simulator

012 prof

012 prof

013 MAC and cheese trophy given to first place winner lily turkstra at the lab's first competitive MAC and cheese cookoff

013 MAC and cheese trophy given to first place winner lily turkstra at the lab's first competitive MAC and cheese cookoff

014 group picture of the bvl team at the MAC and cheese cookoff

014 group picture of the bvl team at the MAC and cheese cookoff

015 bvl team members enjoying a cake to celebrate their successful neur IP s submissions

015 bvl team members enjoying a cake to celebrate their successful neur IP s submissions

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