Deep learning-based control of electrically evoked activity in human visual cortex

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.