Rather than aiming to one day restore natural vision, we might be better off thinking about how to create practical and useful artificial vision now.

Neuromorphic event-based vision sensors may soon power low vision aids and retinal implants, where the visual scene has to be processed quickly and efficiently before it is displayed.

Embedding simulated prosthetic vision models in immersive virtual reality allows sighted subjects to act as virtual patients by “seeing” through the eyes of the patient.

We present a SNN model that uses spike-latency coding and winner-take-all inhibition to efficiently represent visual objects with as little as 15 spikes per neuron.

Rather than aiming to represent the visual scene as naturally as possible, a Smart Bionic Eye could provide visual augmentations through the means of artificial intelligence–based scene understanding, tailored to specific real-world tasks that are known to affect the quality of life of people who are blind.

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.