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Adv."],"published-print":{"date-parts":[[2026,9,4]]},"abstract":"<jats:p>With the growing prevalence of artificial intelligence (AI), demand increases for hardware that mimics the brain\u2019s ability to extract structure from limited data. In the retina, ganglion cells detect features from sparse inputs via lateral inhibition, where neurons antagonistically suppress activity of neighboring cells. Biological neurons exhibit diverse heterogeneous nonlinear responses, linked to robust learning and strong performance in low-data regimes. Here, we introduce a retinally inspired photonic computing system where spatially competing lasing modes in a random network laser act as heterogeneous, inhibitively coupled neurons, enabling feature detection, few-shot classification, and segmentation. 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