“Nanowire Networks Mimic Human Brain’s Learning and Memory Abilities”

An international team led by scientists at the University of Sydney has shown that nanowire networks can project short-term and long-term memories like the human brain.

The research was published today in the journal Science progressLed by dr. Alon Loeffler, Ph.D. in the Faculty of Physics, with collaborators in Japan.

“We found in this study that higher order cognitive functions, which we usually associate with the human brain, can be simulated in non-biological devices,” said Dr. Loeffler.

“This work builds on our previous research in which we demonstrated how nanotechnology can be used to build brain-inspired electrical devices with neural network-like circuits and synapse-like signals.

“Our current work paves the way towards replicating brain-like learning and memory in non-biological hardware systems and suggests that the traits underlying brain-like intelligence may be physical in nature.”

A nanowire network is a type of nanotechnology that is typically made of very fine silver wires that are invisible to the naked eye, covered with a plastic material, and spread over one another like a net. The wires mimic the interconnected aspects of the physical structure of the human brain.

Advances in nanowire networks hold promise for many real-world applications, such as enhancing robots or sensors that need to make quick decisions in unpredictable environments.

“These nanowire networks are similar to artificial neural networks in that the nanowires act like neurons, and the places where they connect to each other are similar to synapses,” said Professor Zdenka Koncic, senior researcher in the School of Physics.

“Instead of doing some sort of machine learning task, in this study Dr. Loeffler took it one step further and tried to prove that nanowire networks display some type of cognitive function.”

To test the networking capabilities of the nanowires, the researchers gave them a test similar to a common memory task used in human psychology experiments, called the N-Back task.

For one person, the N-Back task might involve remembering a particular cat picture from a series of cat pictures displayed in succession. An N-Back score of 7, which is the average of people, indicates that a person can recognize the same image shown seven steps back.

When applied to a network of nanowires, the researchers found that it could “remember” the desired end point in an electrical circuit seven steps back, which translates to a score of 7 on the N-Back test.

“What we’ve done here is manipulate the tip electrode voltage to force the trajectory to change, rather than letting the tissue do its own thing. We force the track to go where we want it to go,” said Dr. Loeffler .

“When we did that, the memory was much higher than resolution and didn’t really degrade over time, which suggests we found a way to harden the pathways to push it where we wanted it, and then the network remembered it.

“Neurologists believe that this is how the brain works, some synaptic connections are strengthened while others are weakened, and it is believed that this is how we remember certain things, how we learn, and so on.”

As the nanowire network continues to be strengthened, the researchers say, it reaches a point where that reinforcement is no longer needed as information is being consolidated into memory.

“It’s kind of a difference between long-term memory and short-term memory in our brains,” said Professor Koncic.

“If we want to remember something for a long period of time, we really need to keep training our brains to consolidate it, otherwise it will fade over time.

“One task demonstrated that nanowire networks could retain up to seven items in memory at a rate well above the chance rate without reinforcement training and near-perfect accuracy with reinforcement training.”