12 citations · 36 across the 6 of their papers we have counts for
6 papers
Evaluating Deep Human-in-the-Loop Optimization for Retinal Implants Using Sighted Participants
Eirini Schoinas, Adyah Rastogi, Anissa Carter +2
Human-in-the-loop optimization (HILO) is a promising approach for personalizing visual prostheses by iteratively refining stimulus parameters based on user feedback. Previous work…
Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses
Jacob Granley, Tristan Fauvel, Matthew Chalk +1
Neuroprostheses show potential in restoring lost sensory function and enhancing human capabilities, but the sensations produced by current devices often seem unnatural or distorted…
Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
Galen Pogoncheff, Jacob Granley, Michael Beyeler
Convolutional neural networks (CNNs) have recently emerged as promising models of the ventral visual stream, despite their lack of biological specificity. While current state-of-th…
Adapting Brain-Like Neural Networks for Modeling Cortical Visual Prostheses
Jacob Granley, Alexander Riedel, Michael Beyeler
Cortical prostheses are devices implanted in the visual cortex that attempt to restore lost vision by electrically stimulating neurons. Currently, the vision provided by these devi…
Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses
Jacob Granley, Lucas Relic, Michael Beyeler
Sensory neuroprostheses are emerging as a promising technology to restore lost sensory function or augment human capabilities. However, sensations elicited by current devices often…
U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina
Shuyun Tang, Ziming Qi, Jacob Granley +1
Fundus photography has routinely been used to document the presence and severity of retinal degenerative diseases such as age-related macular degeneration (AMD), glaucoma, and diab…