Rather than pursuing a (degraded) imitation of natural sight, bionic vision might be better understood as a form of neuroadaptive XR: a perceptual interface that forgoes visual fidelity in favor of delivering sparse, personalized cues shaped (at its full potential) by user intent, behavioral context, and cognitive state.

We present a series of analyses on the shared representations between evoked neural activity in the primary visual cortex of a blind human with an intracortical visual prosthesis, and latent visual representations computed in deep neural networks.

We show that a neurologically-inspired decoding of CNN activations produces qualitatively accurate phosphenes, comparable to phosphenes reported by real patients.

We present a SNN model that uses spike-latency coding and winner-take-all inhibition to efficiently represent visual stimuli from the Fashion MNIST dataset.

We propose HBA-U-Net: a U-Net backbone with hierarchical bottleneck attention to highlight retinal abnormalities that may be important for fovea and optic disc segmentation in the degenerated retina.