Jul 212011

Molecular Soup Exhibits Brainlike Behavior

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.

“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.

Biochemical systems with artificial intelligence — or at least some basic, decision-making capabilities — could have powerful applications in medicine, chemistry, and biological research, the researchers say. In the future, such systems could operate within cells, helping to answer fundamental biological questions or diagnose a disease. Biochemical processes that can intelligently respond to the presence of other molecules could allow engineers to produce increasingly complex chemicals or build new kinds of structures, molecule by molecule.

“Although brainlike behaviors within artificial biochemical systems have been hypothesized for decades,” Qian says, “they appeared to be very difficult to realize.”

The researchers based their biochemical neural network on a simple model of a neuron, called a linear threshold function. The model neuron receives input signals, multiplies each by a positive or negative weight, and only if the weighted sum of inputs surpass a certain threshold does the neuron fire, producing an output. This model is an oversimplification of real neurons, says paper coauthor Erik Winfree, professor of computer science, computation and neural systems, and bioengineering. Nevertheless, it’s a good one. “It has been an extremely productive model for exploring how the collective behavior of many simple computational elements can lead to brainlike behaviors, such as associative recall and pattern completion.”

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