Energy-Efficient Visual Search by Eye Movements with a Low-Latency Spiking Neural Agent

However, whether these features can synergize to produce more energy-efficient computer vision algorithms with human-like eye movements remains underexplored. Here, we record human visual search data and establish an bio-inspired visual search model (BVSM).

To our knowledge, BVSM is a rare SNN-based active visual search agent: it unifies non-uniform retinal sampling, event-driven spiking feature processing and memory, and a learned eye-movement policy within a single closed-loop framework, enabling low-latency saccade decisions in continuous space. The model combines a non-uniform resolution retina with spiking feature extraction, memory, and saccade decision modules, and employs population coding for fast saccade decisions.