Nanowire networks can learn and memorize like the human brain, study says

Researchers demonstrate how nanowire networks exhibit short as well as long-term memory.
Sejal Sharma
Neural network
Neural network


A team of scientists at the University of Sydney have demonstrated that short and long-term memory which is generally associated with the human brain can be reproduced in non-biological hardware as well.

In their study, the researchers found that nanowires, a type of nanotechnology made from tiny and highly conductive wires invisible to the naked eye, mimicked the human brain’s activity in identifying and remembering an image from memory.

How did they achieve this?

Nanowire networks are scattered across like a mesh, mimicking the structure of a human brain. Co-author of the study, Professor Zdenka Kuncic explains that this network acts like a synthetic neural network because the nanowires act like neurons, and the places where they connect with each other are like synapses, where information is passed from one neuron to the next.

Two features that were central to their study were learning and memory. The team used a test called the ‘n-back task’, which is used for measuring memory in human beings. It involves presenting a stimulus sequence, which could be a series of images, and comparing each new entry with one that occurred some steps ago. 

In their press release, they explain: For a person, the n-back task might involve remembering a specific picture of a cat from a series of feline images presented in a sequence. An n-back score of 7, the average for people, indicates the person can recognize the same image that appeared seven steps back.

When the team applied the same n-back task test to the nanowire network, the researchers found it could ‘remember’ previous signals for at least seven steps. Meaning a score of 7 in an n-back test, which is regarded as the average number of items humans can keep in working memory at one time.

And upon reinforcement, the researchers saw a massive improvement. The nanowire network could reach a point where reinforcement is not needed because the information was now a part of its memory.

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

“Our current work 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,” said Dr. Alon Loeffler, a co-author of the study, which was published in Science Advances

Study abstract:

Nanowire networks (NWNs) mimic the brain’s neurosynaptic connectivity and emergent dynamics. Consequently, NWNs may also emulate the synaptic processes that enable higher-order cognitive functions such as learning and memory. A quintessential cognitive task used to measure human working memory is the n-back task. In this study, task variations inspired by the n-back task are implemented in a NWN device, and external feedback is applied to emulate brain-like supervised and reinforcement learning. NWNs are found to retain information in working memory to at least n = 7 steps back, remarkably similar to the originally proposed “seven plus or minus two” rule for human subjects. Simulations elucidate how synapse-like NWN junction plasticity depends on previous synaptic modifications, analogous to “synaptic metaplasticity” in the brain, and how memory is consolidated via strengthening and pruning of synaptic conductance pathways.

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