Both nanowires and memristors are part of the history of research into neural networks and artificial intelligence (AI). Researchers have been investigating the use of nanowires in building electronic mesheson which nerve tissues can be grown; the mesh, they hope, could link nerve cells with electronics. And almost from the time memristors were first isolated and characterized, researchers have been looking at using them in chips that would lead to artificial intelligence.

Professor John Boland, director of CRANN, and his colleagues will be using the research grant to build on their previous work. They already discovered that when electricity—or other stimuli such as chemicals or light—is applied to a random network of nanowires, it generates a chemical reactions at the junctions where the nanowires cross over each other.

This phenomenon is similar to the way the brain works, in that there are bundles of nerves that cross over one another, forming junctions. Over time, the human brain begins to learn which of these junctions is important and discards the rest.

This is where the memristor aspect of the research becomes critical. As Allen Bellew, one of the CRANN researchers, describes in the video, the nanowires that the CRANN team are working with also display some of the characteristics of memristors, such as their ability to “remember” the charge that has passed through it.

One of the application areas that the CRANN team thinks could benefit from nanowire-based neural networks is facial recognition. At present, digital computing is still pretty ineffective at it, but human brains performs this task well.

This funding from the European Research Council allows me to continue my work to deliver the next generation of computing, which differs from the traditional digital approach,” says Boland in a press release. “The human brain is neurologically advanced and exploits connectivity that is controlled by electrical and chemical signals. My research will create nanowire networks that have the potential to mimic aspects of the neurological functions of the human brain, which may revolutionize the performance of current day computers. It could be truly ground-breaking.