Researchers from UC Santa Barbara, ETH Zurich, and Miguel Hernández University have demonstrated that a deep-learning AI model can improve the precision of bionic eye technology. In a 2024 clinical test in Spain, researchers used the AI model to design electrical stimulation patterns for a 96-channel electrode array temporarily implanted in the visual cortex of a blind 27-year-old man. The AI-designed patterns accurately reproduced targeted brain activity with less electrical current, adapting to the brain's fluctuating resting states to improve phosphene perception.
AI-guided visual cortex stimulation
- ▪The AI-designed electrical stimulation patterns reproduced targeted brain activity patterns more accurately and required less electrical current than alternative stimulation approaches.
- ▪Researchers from UC Santa Barbara, ETH Zurich, and Miguel Hernández University used a deep-learning model to design electrical stimulation patterns for electrodes implanted in a blind participant's visual cortex.
Deep learning neural prediction
- ▪Researchers trained a deep neural network to predict the patterns of brain activity produced by different combinations of electrical stimulation settings in the visual cortex.
- ▪The deep-learning model incorporated measurements of the participant's resting brain activity immediately before each test to predict neural responses to electrical stimulation.
Phosphene perception patterns
- ▪Recorded patterns of neural activity in the study participant's brain were more informative about his perceptual experiences of phosphenes than the electrical stimulation parameters themselves.
- ▪Electrical stimulation of the study participant's visual cortex electrodes caused him to perceive phosphenes, which are spots or shapes of light resembling flashes, stars, or fireworks.
Visual cortical prosthesis development
- ▪The study participant received a temporary brain implant composed of a 96-channel array of electrodes in his visual cortex, which was removed after six months.
- ▪The proof-of-concept study, published in the journal Neuron in August 2026, represents a step toward developing visual cortical prostheses that bypass damaged eyes and optic nerves.
- ▪The clinical testing was conducted in 2024 at Hospital IMED Elche in Spain on a 27-year-old male participant who lost his vision following a traumatic brain injury.
Adaptive brain-responsive stimulation
- ▪The deep-learning model adjusted stimulation patterns based on the study participant's resting brain activity, aiming to keep visual prostheses reliable as neural responses fluctuate daily.
- ▪UC Santa Barbara associate professor Michael Beyeler stated that a useful visual prosthesis must learn how an individual brain responds and adapt the stimulation accordingly.
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