20206-08-07 カリフォルニア大学サンタバーバラ校（UCSB）
UCSB’s Michael Beyeler is using AI to improve visual cortical prostheses, or “bionic eyes.”
Photo Credit:Matt Perko
＜関連情報＞
- https://news.ucsb.edu/2026/022739/deep-learning-refines-how-bionic-eyes-communicate-brain
- https://www.cell.com/neuron/abstract/S0896-6273(26)00535-0
- https://medibio.tiisys.com/137865/
深層学習に基づくヒト視覚野における電気刺激誘発活動の制御 Deep learning-based control of electrically evoked activity in human visual cortex
Pehuén Moure ∙ Jacob Granley ∙ Fabrizio Grani ∙ … ∙ Shih-Chii Liu ∙ Michael Beyeler ∙ Eduardo Fernández
Neuron Published:August 7, 2026
DOI:https://doi.org/10.1016/j.neuron.2026.07.006
Highlights
- Deep learning predicts trial-resolved population activity in the human visual cortex
- Optimized stimulation patterns shape evoked activity with lower currents
- Achievable responses lie on a low-dimensional intrinsic neural manifold
- Population activity predicts perceptual reports better than stimulation alone
Summary
Visual cortical prostheses offer a promising path to sight restoration, but current systems elicit crude, variable percepts and rely on manual electrode-by-electrode calibration that does not scale. These limitations reflect a deeper challenge: electrical microstimulation evokes nonlinear, state-dependent population responses in the human visual cortex, complicating the link between stimulation and perception. Here, we present a deep learning framework that leverages a bidirectional cortical implant to causally shape stimulation-evoked population activity in the human visual cortex. The framework, trained on trial-resolved neural recordings, supports two complementary control strategies: a learned inverse network for real-time stimulation synthesis and a gradient-based optimizer for precise targeting. Both outperform conventional methods, achieve targets at lower stimulation currents, and elicit more consistent perception. Achievable responses lie on the intrinsic low-dimensional manifold of cortical activity, and recorded population activity predicts reported percepts substantially better than stimulation parameters alone. Together, these results provide a population-level foundation for linking microstimulation, cortical activity, and perception in the human visual system.
