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 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.
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.
Lucas Nadolskis, Lily M. Turkstra, Ebenezer Larnyo, Michael Beyeler Translational Vision Science & Technology (TVST)
(Note: LN and LMT contributed equally to this work.)
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.
Jacob Granley, Galen Pogoncheff, Alfonso Rodil, Leili Soo, Lily M. Turkstra, Lucas Nadolskis, Arantxa Alfaro Saez, Cristina Soto Sanchez, Eduardo Fernandez Jover, Michael Beyeler Workshop on Representational Alignment (Re-Align), ICLR ‘24
(Note: JG and GP contributed equally to this work.)
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.
Yuchen Hou, Devyani Nanduri, Jacob Granley, James D. Weiland, Michael Beyeler Journal of Neural Engineering
We propose a personalized stimulus encoding strategy that combines state-of-the-art deep stimulus encoding with preferential Bayesian optimization.
Jacob Granley, Tristan Fauvel, Matthew Chalk, Michael Beyeler 37th Conference on Neural Information Processing Systems (NeurIPS) ‘23
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.
Aiwen Xu, Yuchen Hou, Cristopher M. Niell, Michael Beyeler 37th Conference on Neural Information Processing Systems (NeurIPS) ‘23
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.
Lucas Relic, Bowen Zhang, Yi-Lin Tuan, Michael Beyeler ACM Augmented Humans (AHs) ‘22
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.
Nicole Han, Sudhanshu Srivastava, Aiwen Xu, Devi Klein, Michael Beyeler ACM Augmented Humans (AHs) ‘21
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.
Yuchen Hou, Laya Pullela, Jiaxin Su, Sriya Aluru, Shivani Sista, Xiankun Lu, Michael Beyeler IEEE EMBC ‘24
(Note: YH and LP contributed equally to this work.)
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.
Jacob Granley, Galen Pogoncheff, Alfonso Rodil, Leili Soo, Lily M. Turkstra, Lucas Nadolskis, Arantxa Alfaro Saez, Cristina Soto Sanchez, Eduardo Fernandez Jover, Michael Beyeler Workshop on Representational Alignment (Re-Align), ICLR ‘24
(Note: JG and GP contributed equally to this work.)
We present a systematic literature review of 227 publications from 106 different venues assessing the potential of XR technology to further visual accessibility.
Justin Kasowski, Byron A. Johnson, Ryan Neydavood, Anvitha Akkaraju, Michael Beyeler Journal of Vision 23(5):5, 1–24
(Note: JK and BAJ are co-first authors.)
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.
Kexin Chen, Michael Beyeler, Jeffrey L. Krichmar Journal of Neuroscience
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.
Jacob Granley, Lucas Relic, Michael Beyeler 36th Conference on Neural Information Processing Systems (NeurIPS) ‘22
We optimize electrode arrangement of epiretinal implants to maximize visual subfield coverage.
Ashley Bruce, Michael Beyeler Medical Image Computing and Computer Assisted Intervention (MICCAI) ‘22
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.
Nicole Han, Sudhanshu Srivastava, Aiwen Xu, Devi Klein, Michael Beyeler ACM Augmented Humans (AHs) ‘21