Although the eye may appear static during fixation, it is in constant motion–transforming visual input into rich spatio-temporal streams of neuronal activity. These movements challenge the classical view that visual acuity derives from spatial sampling, suggesting that hyperacuity can emerge from precise temporal encoding. Here we show that artificial systems can exploit this principle using retina-like event-based (EB) sensing, a neuromorphic approach in which the sensor emits asynchronous spikes in response to luminance changes. Using an EB camera undergoing controlled “fixational” motion over tiny, pixelated images, we generated datasets in which recognition depends on sub-pixel information. We found that artificial neural networks trained on these spatio-temporal event streams relied crucially on the precise temporal information and outperformed conventional frame-based models. The learned representations also supported Vernier-style sub-pixel discrimination, demonstrating hyperacuity-like behavior in the artificial bio-mimetic system. These results show that active event-based sensing can use precise timing to recover spatial details that are unrecoverable from a static single frame. Together, these findings offer a new perspective on visual perception, open pathways for advancing neuromorphic engineering and energy-efficient AI vision systems, and provide a framework for testing hypotheses about biological vision. Unlike conventional frame-based vision, biomimetic event-based sensing preserves precise temporal information. The authors show that these timing signals can be exploited to recover spatial details from tiny images, enabling event-based vision to outperform frame-based approaches.
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