To build the DNA neural network, the researchers used a process called a strand-displacement cascade. Previously, the team developed this technique to create the largest and most complex DNA circuit yet, one that computes square roots.
This method uses single and partially double-stranded DNA molecules. The latter are double helices, one strand of which sticks out like a tail. While floating around in a water solution, a single strand can run into a partially double-stranded one, and if their bases (the letters in the DNA sequence) are complementary, the single strand will grab the double strand's tail and bind, kicking off the other strand of the double helix. The single strand thus acts as an input while the displaced strand acts as an output, which can then interact with other molecules.
Because they can synthesize DNA strands with whatever base sequences they want, the researchers can program these interactions to behave like a network of model neurons. By tuning the concentrations of every DNA strand in the network, the researchers can teach it to remember the unique patterns of yes-or-no answers that belong to each of the four scientists. Unlike with some artificial neural networks that can directly learn from examples, the researchers used computer simulations to determine the molecular concentration levels needed to implant memories into the DNA neural network.
While this proof-of-principle experiment shows the promise of creating DNA-based networks that canin essencethink, this neural network is limited, the researchers say. The human brain consists of 100 billion neurons, but creating a network with just 40 of these DNA-based neuronsten times larger than the demonstrated networkwould be a challenge, according to the researchers. Furthermore, the system is slow; the test
|Contact: Deborah Williams-Hedges|
California Institute of Technology