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Synapse on a Chip

Nanoscale Memristor

The memristor — the so-called “missing link of electronics” memory technology that can change its resistance in varying levels — has been around on paper for nearly 40 years. However it wasn’t until 2010 that a group at the University of Michigan led by Dr. Wei Lu demonstrated that it can be used to build brain-like computers in a paper just published in Nano Letters. New Scientist reports that “memristors can behave uncannily like the junctions between neurons in the brain.” Scientific American describes a US military-funded project that is trying to use the memristor “to make neural computing a reality.” DARPA’s Systems of Neuromorphic Adaptive Plastic Scalable Electronics Program (SyNAPSE) is funded to create “electronic neuromorphic machine technology that is scalable to biological levels.”

The discovery of the memristor derives from the search for a rigorous mathematical foundation for electronics by a young electronics engineer at the University of California, Berkeley, Leon O. Chua. Chua’s analysis suggested there was a fourth foundational circuit element missing from the standard trio of resistor, capacitor and inductor. He called it “memristor.” In 1971, he published a seminal paper on this missing basic circuit element.

In 1976, Chua and Sung-Mo Kang published another paper describing a large class of devices and systems they called “memristive devices and systems.” The proof of the existence of such devices proved elusive until 2008, when R. Stanley Williams and his research team at HP actually developed a two-terminal titanium dioxide nanoscale device that exhibited memristor characteristics. A memristance device requires its atoms to change location when voltage is applied, and that happens much more easily at the nanoscale (1 x 10-9 meters). Here is HP Senior Fellow R. Stanley Williams at the whiteboard describing the memristor:

HP sees an immediate application of memristors for a new kind of computer memory that could be used in place of the dynamic random access memory (DRAM) commonly found in today’s desktop and laptop computers. When you turn your computer off, whatever you are working on in conventional DRAM is lost unless you save it. With memristor-based computers, your document or spreadsheet (or other data) would be stored without having to save it, with little power drained from the computer’s battery. HP is obviously interested in marketing memristors for computers, cell phones, video games — anything that requires a lot of memory without a lot of battery drain.

The National Institute of Standards and Technology (NIST) promotes U.S. innovation and industrial competitiveness and is also very interested in memristor technology. Here’s a video of Dr. Nadine Gergel-Hackett, who researches memristor technology there, describing the how memristors can be used to develop flexible chips that “do the twist”:

As flexible chips that retain their memory when turned off, clearly memristors could soon have a big impact on the electronics marketplace. But what makes them so well suited to build “brain-like computers?” An HP Labs article makes the case that memristor technology “could one day lead to computer systems that can remember and associate patterns in a way similar to how people do,” Improved facial recognition technology and possibly “provide more complex biometric recognition systems [that] could enable appliances that learn from experience and computers that can make decisions.”

Memristors will soon have a big impact on the electronics marketplace. But what makes them so well suited to build “brain-like computers”?

The remarkable characteristics of the memristor that make it interesting to HP, DARPA, and NIST as a neural computing substrate come from an unlikely source in the biological world: the slime mold Physarum polycephalum. The slime mold appears as a fungus-like gel during certain phases of its lifecycle, hence the name. Like ectoplasm from the movie Ghostbusters, this glutinous single-celled organism — without a single neuron to its name — can sense and react to its environment and even solve simple puzzles. It can also anticipate events.

When Leon Chua first discovered this missing foundational circuit element, he suspected that memristors might have something to do with how biological organisms learn. Experiments with slime molds in 2008 by Tetsu Saisuga at Hokkaido University in Sapporo sparked additional research at the University of California, San Diego by Max Di Ventra. Di Ventra was familiar with Chua’s work and built a memristive circuit that was able to learn and predict future signals. This ability turns out to be similar to the electrical activity involved in the ebb and flow of potassium and sodium ions across cellular membranes: synapses altering their response according to the frequency and strength of signals. New Scientist reports that Di Ventra’s work confirmed Chua’s suspicions that “synapses were memristors.” “The ion channel was the missing circuit element I was looking for,” says Chua, “and it already existed in nature.”

Jumping forward to 2010, the work of Dr. Wei Lu’s University of Michigan team now confirms that memristor circuits indeed behave like synapses. Lu’s team used a mixture of silicon and silver to join two metal electrodes, mimicking how synapses allow neurons to learn new firing patterns — not unlike a slime mold’s ability to anticipate events. The timing of electrical signals in two neurons anticipates how later messages can jump across the synapse between them. When a pair fires, the given synapse becomes more likely to pass later messages between the two. “Cells that fire together, wire together,” says Lu.

Photo: Patrick Landmann/SPL - newscientist.comJust like a synapse, the memristor changes its resistance in varying levels. Dr. Lu found that memristors can simulate synapses because electrical synaptic connections between two neurons can seemingly strengthen or weaken depending on when the neurons fire. “The memristor mimics synaptic action,” Lu concludes. Dr. Nadine Gergel-Hackett at NIST acknowledges the Michigan team’s successful creation of a brain synapse analog. “This work is a large step towards the realization of biology-inspired computing,” she says.

The human brain contains about 10 billion nerve cells, or neurons. On average, each neuron is connected to other neurons through about 10,000 synapses. While Lu’s research is promising, it will likely be a while until researchers can demonstrate circuits with even tens of thousands of memristor “synapses.” Nevertheless, DARPA’s SyNAPSE project appears committed to scaling memristor technology to biological levels.