Even as some scientists and engineers develop improved versions of current computing technology, others are looking into drastically different approaches. DNA computing offers the potential of massively parallel calculations with low power consumption and at small sizes. Research in this area has been limited to relatively small systems, but a group from Caltech recently constructed DNA logic gates using over 130 different molecules and used the system to calculate the square roots of numbers. Now, the same group published a paper in Nature that shows an artificial neural network, consisting of four neurons, created using the same DNA circuits.

The artificial neural network approach taken here is based on the perceptron model, also known as a linear threshold gate. This models the neuron as having many inputs, each with its own weight (or significance). The neuron is fired (or the gate is turned on) when the sum of each input times its weight exceeds a set threshold. These gates can be used to construct compact Boolean logical circuits, and other circuits can be constructed to store memory.

As we described in the last article on this approach to DNA computing, the authors represent their implementation with an abstraction called "seesaw" gates. This allows them to design circuits where each element is composed of two base-paired DNA strands, and the interactions between circuit elements occurs as new combinations of DNA strands pair up. The ability of strands to displace each other at a gate (based on things like concentration) creates the seesaw effect that gives the system its name.

In order to construct a linear threshold gate, three basic seesaw gates are needed to perform different operations. Multiplying gates combine a signal and a set weight in a seesaw reaction that uses up fuel molecules as it converts the input signal into output signal. Integrating gates combine multiple inputs into a single summed output, while thresholding gates (which also require fuel) send an output signal only if the input exceeds a designated threshold value. Results are read using reporter gates that fluoresce when given a certain input signal.

To test their designs with a simple configuration, the authors first constructed a single linear threshold circuit with three inputs and four outputs—it compared the value of a three-bit binary number to four numbers. The circuit output the correct answer in each case.

For the primary demonstration on their setup, the authors had their linear threshold circuit play a computer game that tests memory. They used their approach to construct a four-neuron Hopfield network, where all the neurons are connected to the others and, after training (tuning the weights and thresholds) patterns can be stored or remembered. The memory game consists of three steps: 1) the human chooses a scientist from four options (in this case, Rosalind Franklin, Alan Turing, Claude Shannon, and Santiago Ramon y Cajal); 2) the human “tells” the memory network the answers to one or more of four yes/no (binary) questions used to identify the scientist (such as, “Did the scientist study neural networks?” or "Was the scientist British?"); and 3) after eight hours of thinking, the DNA memory guesses the answer and reports it through fluorescent signals.

They played this game 27 total times, for a total of 81 possible question/answer combinations (34). You may be wondering why there are three options to a yes/no question—the state of the answers is actually stored using two bits, so that the neuron can be unsure about answers (those that the human hasn't provided, for example) using a third state. Out of the 27 experimental cases, the neural network was able to correctly guess all but six, and these were all cases where two or more answers were not given.

In the best cases, the neural network was able to correctly guess with only one answer and, in general, it was successful when two or more answers were given. Like the human brain, this network was able to recall memory using incomplete information (and, as with humans, that may have been a lucky guess). The network was also able to determine when inconsistent answers were given (i.e. answers that don’t match any of the scientists).

These results are exciting—simulating the brain using biological computing. Unlike traditional electronics, DNA computing components can easily interact and cooperate with our bodies or other cells—who doesn’t dream of being able to download information into your brain (or anywhere in your body, in this case)? Even the authors admit that it’s difficult to predict how this approach might scale up, but I would expect to see a larger demonstration from this group or another in the near future.

Nature, 2011. DOI: 10.1038/nature10262 (About DOIs)