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

Human vision incorporates a non-uniform resolution retina, efficient eye movement strategies, and spiking neural networks (SNNs) to balance requirements in visual field size, visual resolution, energy cost, and inference latency. 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. It can learn either a human-like or near-optimal fixation strategy by reinforcement learning, outperform human search performance, and achieve high energy efficiency through low saccade decision latency and sparse activation. Our findings suggest that SNNs with non-uniform resolution retina and efficient eye-movement strategies can facilitate more energy-efficient computer vision algorithms, especially when a large visual field, high visual resolution, and low energy cost are simultaneously required.