We developed a data-driven neural control framework for a visual cortical prosthesis in a blind human, showing that deep learning can synthesize efficient, stable stimulation patterns that reliably evoke percepts and outperform conventional calibration methods.
Topic: Machine Learning
Researchers Interested in This Topic
CS
Ally Chu
BS/MS Student
CS
Jacob Granley
Postdoctoral Researcher
CS
Yuchen Hou
PhD Student
CS
Srinandha Murugesan
Research Assistant
DYNS
Lucas Nadolskis
PhD Student
CS
Galen Pogoncheff
PhD Student
EE
Magnolia Saur
UC LEADS Scholar
Research Projects
Deep learning-based control of electrically evoked activity in human visual cortex
Pehuén Moure, Jacob Granley, Fabrizio Grani, Leili Soo, Antonio Lozano, Rocio López-Peco, Adrián Villamarin-Ortiz, Cristina Soto-Sánchez, Shih-Chii Liu, Michael Beyeler, Eduardo Fernández bioRxiv
(Note: PM, JG, and FG are co-first authors. SL, MB, and EF are co-last authors.)
Mouse vs. AI: A neuroethological benchmark for visual robustness and neural alignment
We propose the Mouse vs. AI: Robust Foraging Competition at NeurIPS ‘25, a novel bioinspired visual robustness benchmark to test generalization in reinforcement learning (RL) agents trained to navigate a virtual environment toward a visually cued target.
Marius Schneider, Joe Canzano, Jing Peng, Yuchen Hou, Spencer LaVere Smith, Michael Beyeler arXiv:2509.14446
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?
Cortical Visual Processing for Navigation
How does the brain extract relevant visual features from the rich, dynamic visual input that typifies active exploration, and how does the neural representation of these features support visual navigation?
