Criada primeira rede neural artificial a partir do DNA: exibe comportamento tipo cérebro

sexta-feira, julho 22, 2011

First Artificial Neural Network Created out of DNA: Molecular Soup Exhibits Brainlike Behavior

ScienceDaily (July 20, 2011) — Artificial intelligence has been the inspiration for countless books and movies, as well as the aspiration of countless scientists and engineers. 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.

Caltech researchers have invented a method for designing systems of DNA molecules whose interactions simulate the behavior of a simple mathematical model of artificial neural networks. (Credit: Caltech/Lulu Qian)

"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. "What we are good at is recognizing things," says coauthor Jehoshua "Shuki" Bruck, the Gordon and Betty Moore Professor of Computation and Neural Systems and Electrical Engineering. "We can recognize things based on looking only at a subset of features." The DNA neural network does just that, albeit in a rudimentary way.

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Neural network computation with DNA strand displacement cascades

Lulu Qian, Erik Winfree & Jehoshua Bruck



Corresponding author

Nature 475, 368–372 (21 July 2011) doi:10.1038/nature10262

Received 31 December 2010 Accepted 31 May 2011 Published online 20 July 2011

The impressive capabilities of the mammalian brain—ranging from perception, pattern recognition and memory formation to decision making and motor activity control—have inspired their re-creation in a wide range of artificial intelligence systems for applications such as face recognition, anomaly detection, medical diagnosis and robotic vehicle control1. Yet before neuron-based brains evolved, complex biomolecular circuits provided individual cells with the ‘intelligent’ behaviour required for survival2. However, the study of how molecules can ‘think’ has not produced an equal variety of computational models and applications of artificial chemical systems. Although biomolecular systems have been hypothesized to carry out neural-network-like computations in vivo3, 2, 4 and the synthesis of artificial chemical analogues has been proposed theoretically5, 6, 7, 8, 9, experimental work10, 11, 12, 13 has so far fallen short of fully implementing even a single neuron. Here, building on the richness of DNA computing14 and strand displacement circuitry15, we show how molecular systems can exhibit autonomous brain-like behaviours. Using a simple DNA gate architecture16 that allows experimental scale-up of multilayer digital circuits17, we systematically transform arbitrary linear threshold circuits18 (an artificial neural network model) into DNA strand displacement cascades that function as small neural networks. Our approach even allows us to implement a Hopfield associative memory19 with four fully connected artificial neurons that, after training in silico, remembers four single-stranded DNA patterns and recalls the most similar one when presented with an incomplete pattern. Our results suggest that DNA strand displacement cascades could be used to endow autonomous chemical systems with the capability of recognizing patterns of molecular events, making decisions and responding to the environment.

Subject terms: Information technology, Neuroscience, Biotechnology, Molecular biology


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