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A four-artificial-neuron network from 112 DNA strands

Following up on their recent accomplishment of building a computational circuit from 74 small DNA molecules, Caltech researchers assembled 112 DNA strands into four artificial neurons that they trained with four pieces of information about four scientists. The artificial neural network can then play a game in which it properly answers questions about the identity of a scientist that the player has in mind even when the player gives it incomplete or wrong information. In this way it mimics the ability of a brain to make decisions based on incomplete information. The research was published in Nature [abstract]. The researchers have produced videos that explain their work (Part I: design; Part II: experiments). From a Caltech news release written by Marcus Woo “Caltech Researchers Create the First Artificial Neural Network Out of DNA: Molecular Soup Exhibits Brainlike Behavior“:

… Researchers at the California Institute of Technology (Caltech) have now taken a major step toward creating artificial intelligence—not in a robot or a silicon chip, but in a test tube. The researchers are the first to have made an artificial neural network out of DNA, creating a circuit of interacting molecules that can recall memories based on incomplete patterns, just as a brain can.

“The brain is incredible,” says Lulu Qian, a Caltech senior postdoctoral scholar in bioengineering and lead author on the paper describing this work, published in the July 21 issue of the journal Nature. “It allows us to recognize patterns of events, form memories, make decisions, and take actions. So we asked, instead of having a physically connected network of neural cells, can a soup of interacting molecules exhibit brainlike behavior?”

The answer, as the researchers show, is yes.

Consisting of four artificial neurons made from 112 distinct DNA strands, the researchers’ neural network plays a mind-reading game in which it tries to identify a mystery scientist. The researchers “trained” the neural network to “know” four scientists, whose identities are each represented by a specific, unique set of answers to four yes-or-no questions, such as whether the scientist was British.

After thinking of a scientist, a human player provides an incomplete subset of answers that partially identifies the scientist. The player then conveys those clues to the network by dropping DNA strands that correspond to those answers into the test tube. Communicating via fluorescent signals, the network then identifies which scientist the player has in mind. Or, the network can “say” that it has insufficient information to pick just one of the scientists in its memory or that the clues contradict what it has remembered. The researchers played this game with the network using 27 different ways of answering the questions (out of 81 total combinations), and it responded correctly each time.

This DNA-based neural network demonstrates the ability to take an incomplete pattern and figure out what it might represent—one of the brain’s unique features. …

This is a promising milestone on the way to developing molecular computers to function inside living cells—analyzing the molecular environment inside the cell and taking appropriate action.

Paper coauthor and Caltech professor Erik Winfree and colleague Paul W.K. Rothemund won the 2006 Feynman Prizes in Nanotechnology for both the experimental work and theory categories.

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