Previously, the team developed this technique to create the largest and most complex DNA circuit yet, one that computes square roots. To build the DNA neural network, the researchers used a process called a strand-displacement cascade. "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." This model is an oversimplification of real neurons, says paper coauthor Erik Winfree, professor of computer science, computation and neural systems, and bioengineering. 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. The researchers based their biochemical neural network on a simple model of a neuron, called a linear threshold function.
"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. "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. This DNA-based neural network demonstrates the ability to take an incomplete pattern and figure out what it might representone of the brain's unique features. 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. 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. Communicating via fluorescent signals, the network then identifies which scientist the player has in mind. The player then conveys those clues to the network by dropping DNA strands that correspond to those answers into the test tube. 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.Īfter thinking of a scientist, a human player provides an incomplete subset of answers that partially identifies the scientist. The answer, as the researchers show, is yes.Ĭonsisting 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. So we asked, instead of having a physically connected network of neural cells, can a soup of interacting molecules exhibit brainlike behavior?" "It allows us to recognize patterns of events, form memories, make decisions, and take actions. "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.
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