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An international team of scientists, led by the University of Sydney, has discovered that nanowire networks can exhibit both short- and long-term memory, similar to the human brain.

The findings have been published in the journal Science Advances, led by Dr Alon Loeffler, who received his PhD in the School of Physics, with collaborators in Japan.

Dr Loeffler stated, “In this research we found higher-order cognitive function, which we normally associate with the human brain, can be emulated in non-biological hardware.”

This research builds on previous studies that showed how nanotechnology could be used to build a brain-inspired electrical device with neural network-like circuitry and synapse-like signalling.

The study paves the way towards 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 networks are a type of nanotechnology typically made from tiny, highly conductive silver wires that are invisible to the naked eye, covered in a plastic material, which are scattered across each other like a mesh. The wires mimic aspects of the networked physical structure of a human brain.

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

Senior author Professor Zdenka Kuncic, from the School of Physics, said, “This nanowire network is like a synthetic neural network because the nanowires act like neurons, and the places where they connect with each other are analogous to synapses.”

The researchers gave the nanowire network a test similar to a common memory task used in human psychology experiments, called the N-Back task, to test its capabilities.

The researchers found that the nanowire network could ‘remember’ a desired endpoint in an electric circuit seven steps back, meaning a score of 7 in an N-Back test.

Dr Loeffler said, “When we implement that, its memory had much higher accuracy and didn’t really decrease over time, suggesting that we’ve found a way to strengthen the pathways to push them towards where we want them, and then the network remembers it.”

The researchers found that when the nanowire network is constantly reinforced, it reaches a point where that reinforcement is no longer needed because the information is consolidated into memory.

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

The study shows that nanowire networks can store up to seven items in memory at substantially higher than chance levels without reinforcement training and near-perfect accuracy with reinforcement training.


Source material by: University of Sydney. Content has been edited for better readability.