At the University of Sydney, an international research group has illustrated that nanowire networks could display short- and long-term memory similar to the human brain.
Nanowire network. Image Credit: Alon Loeffler.
The study has been reported in the Science Advances journal, headed by Dr. Alon Loeffler, who received his Ph.D. in the School of Physics with partners in Japan.
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 work builds on our previous research in which we showed how nanotechnology could be used to build a brain-inspired electrical device with neural network-like circuitry and synapse-like signaling.
Dr. Alon Loeffler, School of Physics, The University of Sydney
Loeffler added, “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.”
Nanowire networks, a type of nanotechnology, are normally made from small and highly conductive silver wires that are invisible to the naked eye and covered in a plastic material, which is dispersed across each other like a mesh. The wires imitate aspects of the networked physical structure of a human brain.
Progression in the field of nanowire networks could support the fabrication of many real-world applications, such as improving robotics or sensor devices that need to make quick decisions in unpredictable environments.
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.
Zdenka Kuncic, Study Senior Author and Professor, School of Physics, The University of Sydney
Kuncic added, “Instead of implementing some kind of machine learning task, in this study Dr. Loeffler has actually taken it one step further and tried to demonstrate that nanowire networks exhibit some kind of cognitive function.”
To analyze the abilities of the nanowire network, the team used a common memory test that is often used in human psychology experiments called the N-Back task.
For a person, the N-Back task may involve remembering a particular picture of a cat from a range of feline images presented in a sequence. An N-Back score of 7, the average for people, denotes that the person has the potential to identify the same image that appeared seven steps back.
When employed in the nanowire network, the scientists discovered it could “remember” a preferred endpoint in an electric circuit seven steps back. This implies a score of 7 on an N-Back test.
What we did here is manipulate the voltages of the end electrodes to force the pathways to change, rather than letting the network just do its own thing. We forced the pathways to go where we wanted them to go.
Dr. Alon Loeffler, School of Physics, The University of Sydney
Loeffler continued, “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.
“Neuroscientists think this is how the brain works, certain synaptic connections strengthen while others weaken, and that's thought to be how we preferentially remember some things, how we learn and so on,” added Loeffler.
The researchers stated 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 stated, “It's kind of like the difference between long-term memory and short-term memory in our brains. If we want to remember something for a long period of time, we really need to keep training our brains to consolidate that, otherwise it just kind of fades away over time."
Professor Kuncic added, “One task showed that the nanowire network can store up to seven items in memory at substantially higher than chance levels without reinforcement training and near-perfect accuracy with reinforcement training.”
Journal Reference:
Loeffler, A., et al. (2023) Neuromorphic learning, working memory, and metaplasticity in nanowire networks. Science Advances. doi.org/10.5281/zenodo.7633957.