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

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.