Two years ago this month, HP Labs announced that they had developed a switching memristor, a long-theorized fundamental electronic component that “remembers” the value of the current flowing through it even after the current was switched off. Now, University of Michigan computer engineer Wei Lu and his colleagues are exploring memristors as the foundation for the memory and learning functions in a future electronic brain modeled on that of a cat. (For info on other efforts and controversies around cat brain simulations, see IEEE Spectrum’s “The Cat Brain Cliff Notes“) In Lu’s work, the memristors act as synapses. The researchers published their results in the scientific journal Nano Letters. From the University of Michigan:
“We are building a computer in the same way that nature builds a brain,” said Lu, an assistant professor in the U-M Department of Electrical Engineering and Computer Science. “The idea is to use a completely different paradigm compared to conventional computers. The cat brain sets a realistic goal because it is much simpler than a human brain but still extremely difficult to replicate in complexity and efficiency…”
So far, Lu has connected two electronic circuits with one memristor. He has demonstrated that this system is capable of a memory and learning process called “spike timing dependent plasticity.” This type of plasticity refers to the ability of connections between neurons to become stronger based on when they are stimulated in relation to each other. Spike timing dependent plasticity is thought to be the basis for memory and learning in mammalian brains.
“We show that we can use voltage timing to gradually increase or decrease the electrical conductance in this memristor-based system. In our brains, similar changes in synapse conductance essentially give rise to long term memory,” Lu said.
The next step is to build a larger system, Lu said. His goal is achieve the sophistication of a supercomputer in a machine the size of a two-liter beverage container. That could be several years away.