An important property of any neural network is the ability to learn by taking in information and retaining it for future decisions. Now, researchers in the laboratory of Lulu Qian, professor of bioengineering, have created a DNA-based neural network that can learn. The work represents a first step toward demonstrating more complex learning behaviors in chemical systems.
A paper describing the research appears in the journal Nature. Kevin Cherry (PhD ’24) is the study’s first author.
The ability to learn is found on many scales: Our brains rewire themselves to integrate new information, our immune systems chemically encode information about encounters with pathogens for the future, and even single-celled bacteria learn simple information about chemical gradients and use it to navigate toward food. Learning is a key component of intelligence, whether natural or artificial; for example, “smart” devices can learn your preferences and offer customized recommendations.
“Our goal was to build a molecular system from scratch that could take in examples, find the underlying patterns, and then act on new information it had never seen before,” Qian says. “Think of a future artificial cell with a biological cell as its teacher. It observes how the teacher reacts to different molecular cues, stores those experiences, and—over the course of many lessons—figures out how to respond on its own to similar but not identical cues.”
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