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. "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 intelligenceor at least some basic, decision-making capabilitiescould 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
|Contact: Deborah Williams-Hedges|
California Institute of Technology