"In this work, we have designed and created biochemical circuits that function like a small network of neurons to classify molecular information substantially more complex than previously possible," said Lulu Qian, assistant professor at California Institute of Technology in the US.
To illustrate the capability of DNA-based neural networks, researchers chose a task that is a classic challenge for electronic artificial neural networks: recognising handwriting.
Human handwriting can vary widely, and so when a person scrutinises a scribbled sequence of numbers, the brain performs complex computational tasks in order to identify them.
Artificial neural networks networks must be "taught" how to recognise numbers, account for variations in handwriting, then compare an unknown number to their so-called memories and decide the number's identity.
In the study published in the journal Nature, researchers showed that a neural network made out of carefully designed DNA sequences could carry out prescribed chemical reactions to accurately identify "molecular handwriting."
Unlike visual handwriting that varies in geometrical shape, each example of molecular handwriting does not actually take the shape of a number.
Instead, each molecular number is made up of 20 unique DNA strands chosen from 100 molecules, each assigned to represent an individual pixel in any 10 by 10 pattern. These DNA strands are mixed together in a test tube.
Given a particular example of molecular handwriting, the DNA neural network can classify it into up to nine categories, each representing one of the nine possible handwritten digits from 1 to 9.
Researchers plan to develop artificial neural networks that can learn, forming "memories" from examples added to the test tube. This way the same smart soup can be trained to perform different tasks, Qian said.
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