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.