Biomimetic hardware development demonstrates adaptive color recognition using retina-inspired spike processing, suggesting efficient vision systems for dynamic robotic interactions.
Conventional machine vision systems are hindered by constrained adaptability, particularly in dynamic and unpredictable environments. Herein, we present a neuromorphic color recognition system inspired by the intricacies of retinal signal processing, constructed through a hierarchical bio-mimetic framework. The system integrates a broadband photosensor to emulate the spectral selectivity of cone cells and employs an ion-gel-gated oxide transistor to replicate synaptic dynamics, both of which are integral to achieving highly efficient color recognition. In addition, a dual-threshold algorithm is incorporated, enabling precise control of robotic motions. The system's event-driven architecture with a hierarchical coding strategy enhances dynamic perception, collectively rendering it highly adaptive and highly efficient for environmental interactions.
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Gong et al. (2025) studied this question.
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