Scientists Create Device That Works Just like the Human Brain

A team of U.S. electrical engineers has designed a device that efficiently mimics human brain synapses—a possible breakthrough in the field of "neuromorphic computing."

For the first time, the component, known as a neuromorphic memristor, has been proven to carry signals between neurons using very low power—a major challenge in prior studies, researchers said.

Neuromorphic computing is the complex concept of emulating the operation of the biological brain to help computers and artificial intelligence-led machines of the future deal with "uncertainty, ambiguity, and contradiction in the natural world," according to the U.S. technology giant Intel.

"The key challenges in neuromorphic research are matching a human's flexibility, and ability to learn from unstructured stimuli with the energy efficiency of the human brain," it notes.

But now, engineers from the University of Massachusetts Amherst have revealed analysis suggesting that "protein nanowires" harvested from a bacteria known as Geobacter may be the key to unlocking the mystery of how to replicate the low levels of power the human brain uses to send signals.

Researchers said that while conventional computers operate at over one volt, the brain spews signals between neurons at around 80 millivolts–which is many times lower.

The project, the findings of which have been published in Nature Communications, featured the creation of a new type of memristor that uses such protein nanowires to reach similar "neurological voltages."

"This is the first time that a device can function at the same voltage level as the brain," engineering researcher and co-author of the research paper, Jun Yao, said in a statement. "People probably didn't even dare to hope that we could create a device that is as power-efficient as the biological counterparts in a brain but now we have realistic evidence of ultra-low power computing capabilities. It's a concept breakthrough and we think it's going to cause a lot of exploration in electronics that work in the biological voltage regime," Yao continued.

The paper, published April 20, describes how the memristive devices have become "promising candidates to emulate biological computing."

Tianda Fu, Ph.D. candidate in electrical and computer engineering and first author on the study, said the protein nanowires were developed at UMass Amherst by microbiologist and co-author Derek Lovley, who noted protein nanowires in Geobacter are suited to the experience as they are electrically conductive.

According to Fu, the team found that sending pulses of electricity through a metal thread inside a memristor had created new connections similar to learning in the human brain.

"You can modulate the conductivity, or the plasticity of the nanowire-memristor synapse so it can emulate biological components for brain-inspired computing. Compared to a conventional computer, this device has a learning capability that is not software-based," he said.

The research team said it will now seek to "fully explore the chemistry, biology and electronics of protein nanowires in memristors" and how they could have real-world applications.

"We 'borrowed' protein nanowires from [a] bacteria known to facilitate electrochemical reduction of metal and made a protein-type memristor. Boom, it worked," Yao told Newsweek.

"The implication can be profound. We may be able to make computers as power-efficient as the biological brain, so it will break the conventional concept that electronics need to work at higher powers, which will be followed by a broader search of computational devices/architecture that can work at biological voltage.

He added: "[The research] blurs the boundaries between electronics and the biological system. That can lead to advanced brain-machine interface and prosthetics, in which the human body can have more intimate interface or communication with engineered robots/devices."

This article has been updated with comments from Jun Yao, Assistant Professor of Electrical and Computer Engineering at the University of Massachusetts, Amherst.