PhosInv: Learning a Shared Implant Placement and Stimulus-Conditional Electrode Selection for Visual Cortical Prostheses

Visual cortical prostheses aim to restore rudimentary vision by electrically stimulating the primary visual cortex to evoke percepts known as phosphenes. Their design requires determining where to implant a fixed electrode array and which contacts to activate for each target stimulus. Prior work reoptimises implant placement separately for each stimulus, an approach that cannot be implemented once a device has been implanted. We reformulate this task as a generalisation problem: learning a single implant placement shared across a distribution of target stimuli, together with a stimulus-conditional electrode-activation policy. PhosInv implements this formulation as a convolutional inverse model trained end-to-end through a differentiable reimplementation of an existing phosphene simulator. Once trained, it predicts a sparse electrode selection for each new stimulus in a single forward pass, without per-stimulus optimisation. On a benchmark of 2,820 targets from two domains, all scored under the differentiable renderer, domain-matched PhosInv attains lower loss than per-stimulus Bayesian placement search in all six subject hemispheres in both domains. A forward pass takes milliseconds on a GPU against minutes of CPU search per stimulus, a gap that combines amortisation with differences in hardware and implementation, and PhosInv commands six to eight times fewer contacts, reducing delivered charge, a quantity constrained by safety considerations. A model trained on an equal mixture of both domains retains most of each domain-specific model's advantage while avoiding the degradation either shows outside its own domain, indicating that training coverage, not the architecture itself, limits generalisation. Relative to a shared all-contacts-active reference, learned electrode selection contributes about three times the improvement achieved by per-stimulus placement search and far more than a placement searched once per hemisphere, identifying electrode selection as the more consequential lever for future high-density arrays. Every contrast is reported per anatomy, as an effect size and its consistency across six subject hemispheres.