DNA-based neural network learns from examples to solve problems

Neural networks are computing systems designed to mimic both the structure and function of the human brain.

Caltech researchers have been developing a neural network made out of strands of DNA instead of electronic parts that carries out computation through chemical reactions rather than digital signals.

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.

"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."

Image source: Olivier Wyart & Ailadi Cortelletti.