12 citations · 36 across the 10 of their papers we have counts for
11 papers
Predictor Construction Can Reverse Multimodal Neural Contrasts
Lucas Nadolskis, Galen Pogoncheff, Michael Beyeler
Foundation-model features are increasingly used to ask what information neural activity represents, often by comparing prediction gains between nested encoding models. We show that…
Beyond Neural Activity Prediction: Probing Latent Representations in Mouse V1 Digital Twins
Adriano Lima, Yuchen Hou, Michael Beyeler +1
Digital twins of sensory cortex serve as powerful response oracles. Although prediction accuracy is the central metric by which these models are evaluated, it provides limited insi…
Fuzzing the brain: Automated stress testing for the safety of ML-driven neurostimulation
Mara Downing, Matthew Peng, Jacob Granley +2
Objective: Machine learning (ML) models are increasingly used to generate electrical stimulation patterns in neuroprosthetic devices such as visual prostheses. While these models p…
BIRD: Behavior Induction via Representation-structure Distillation
Galen Pogoncheff, Michael Beyeler
Human-aligned deep learning models exhibit behaviors consistent with human values, such as robustness, fairness, and honesty. Transferring these behavioral properties to models tra…
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…
Beyond Sight: Probing Alignment Between Image Models and Blind V1
Jacob Granley, Galen Pogoncheff, Alfonso Rodil +7
Neural activity in the visual cortex of blind humans persists in the absence of visual stimuli. However, little is known about the preservation of visual representation capacity in…
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…