Supervised Learning in DNA Neural Networks

This September 2025 paper article from Nature, authored by Kevin M. Cherry and Lulu Qian, introduces a novel DNA-based neural network capable of supervised learning in vitro. The authors demonstrate how DNA molecules can be programmed to autonomously classify patterns from molecular examples. This system integrates training data directly into molecular memories and uses these memories for subsequent classification, moving beyond previous systems that relied on in silico learning. The work highlights the potential of molecular circuits to perform complex information processing, opening doors for adaptive decision-making in various physical systems, from biomedicine to soft materials. The article meticulously details the design, characterization, and scalability of their DNA neural network, showcasing its robustness and outlining future challenges like unsupervised learning and increased complexity through spatial organization.Source:https://www.nature.com/articles/s41586-025-09479-w

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