An international team led by scientists from the University of Sydney has shown that nanowire networks can exhibit both short-term and long-term memory, just like the human brain. The research was published today in the journal Science Advances Having received his doctorate, Dr. Headed by Alon Loeffler. At the School of Physics with collaborators in Japan.

“In this study, we found that the high-level cognitive function we normally associate with the human brain can be simulated on non-biological hardware,” Leffler said. “This work builds on our previous research, where we demonstrated how nanotechnology can be used to create a brain-inspired electrical device with neural network-like circuitry and synapse-like signaling.

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

Nanowire meshes are a type of nanotechnology made of small, highly conductive silver wires, typically invisible to the naked eye, covered with a plastic material, spread over each other like a mesh. Wires mimic properties of the networked physical structure of the human brain.

Advances in nanowire networks could herald many real-world applications, such as advances in robotics or sensor devices that need to make quick decisions in unpredictable environments.

“This nanowire network looks like a synthetic neural network because the nanowires act like neurons and the places where they connect to each other look like synapses,” said senior author Professor Zdenka Kuncic from the School of Physics. “In this study, instead of doing some kind of machine learning task, Dr. Leffler took it a step further and tried to show that nanowire networks exhibit some kind of cognitive function.”

To test the capabilities of the nanowire mesh, the researchers performed a test similar to a common memory task used in human psychology experiments called the N-Back task.

For a human, the N-Back task may involve recalling a particular cat image from a series of cat images presented in a series. An N-Back score of 7, which is average for humans, means that a person can recognize the same image appearing seven steps back. The researchers found that when applied to a nanowire network, it could “remember” the desired endpoint in an electrical circuit seven steps back, corresponding to a score of 7 on the N-Back test.

“Instead of letting the network do its thing, we manipulated the voltage of the terminal electrodes to change the paths. We took the paths where we wanted them to go,” said Dr. Leffler.

“When we do this, its memory is much more accurate and doesn’t actually degrade over time, which means we find a way to solidify the roads and push them where we want them to go, and then the network remembers that.

“Neuroscientists think that’s how the brain works, that some synaptic connections get stronger and others get weaker, and they think that’s how we remember things, how we learn, etc.”

The researchers say that when a nanowire network is continuously augmented, this augmentation reaches a point where it is no longer needed, as the information is consolidated in memory. “It’s like the difference between long-term and short-term memory in our brains,” Professor Kuncic said.

“If we want to remember something for a long time, we really have to keep training our brain to reinforce it, otherwise it will be forgotten over time. showed that he was able to store the item.”