What do visual prosthesis users see, and why? Clinical studies have shown that the vision provided by current devices differs substantially from normal sight.
Jacob Granley
(he/him)
PhD Candidate
Computer Science
University of California, Santa Barbara
Jacob Granley is a PhD student in the Department of Computer Science.
Prior to joining UCSB, he received his Masters and Bachelors in Computer Science from Colorado School of Mines. He is pursuing his PhD under Dr. Beyeler as part of the Bionic Vision lab, where he hopes to use Computer Science and Machine Learning methods to help improve artificial vision technologies with the ultimate goal of restoring sight to the blind.
- 3201C BioEngineering
- jgranley@ucsb.edu
Education
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MS in Computer Science, 2020
University of Colorado, School of Mines
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BS in Computer Science, 2019
University of Colorado, School of Mines
In the News
Project Lead
End-to-End Optimization of Bionic Vision
Rather than predicting perceptual distortions, one needs to solve the inverse problem: What is the best stimulus to generate a desired visual percept?
pulse2percept: A Python-Based Simulation Framework for Bionic Vision
pulse2percept is an open-source Python simulation framework used to predict the perceptual experience of retinal prosthesis patients across a wide range of implant configurations.
Publications
Explaining V1 properties with a biologically constrained deep learning architecture
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 37th Conference on Neural Information Processing Systems (NeurIPS) ‘23
Human-in-the-loop optimization for deep stimulus encoding in visual prostheses
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
Axonal stimulation affects the linear summation of single-point perception in three Argus II users
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 medRxiv:2023.07.21.23292908v1
Adapting brain-like neural networks for modeling cortical visual prostheses
We show that a neurologically-inspired decoding of CNN activations produces qualitatively accurate phosphenes, comparable to phosphenes reported by real patients.
Jacob Granley, Alexander Riedel, Michael Beyeler Shared Visual Representations in Human & Machine Intelligence (SVRHM) Workshop, NeurIPS ‘22
Hybrid neural autoencoders for stimulus encoding in visual and other sensory neuroprostheses
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
A computational model of phosphene appearance for epiretinal prostheses
We present a phenomenological model that predicts phosphene appearance as a function of stimulus amplitude, frequency, and pulse duration.
Jacob Granley, Michael Beyeler IEEE Engineering in Medicine and Biology Society Conference (EMBC) ‘21
U-Net with hierarchical bottleneck attention for landmark detection in fundus images of the degenerated retina
We propose HBA-U-Net: a U-Net backbone with hierarchical bottleneck attention to highlight retinal abnormalities that may be important for fovea and optic disc segmentation in the degenerated retina.
Shuyun Tang, Ziming Qi, Jacob Granley, Michael Beyeler MICCAI Workshop on Ophthalmic Image Analysis - OMIA ‘21
