Nanowire Network Demonstrates Human-Like Intelligence


On Sydney Universityan international research group has illustrated that nanowire networks can display short-term and long-term memory similar to the human brain.

Nanowire Network Demonstrates Human-Like Intelligence.

Nanowire network. Image Credit: Alon Loeffler.

The study has been reported in Science Advances journal, led by Dr. Alon Loeffler, who received his Ph.D. in the School of Physics with partners in Japan.

In this study we found that high-level cognitive functions, which we usually associate with the human brain, can be emulated in non-biological hardware.. This work builds on our previous research in which we demonstrated how nanotechnology can be used to build brain-inspired electrical devices with neural network-like circuits and signaling like synapses..

Dr. Alon Loeffler, School of Physics, 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 traits underlying brain-like intelligence may be physical in nature..”

Nanowire networks, a type of nanotechnology, are usually made of tiny, highly conductive silver wires invisible to the naked eye and covered with a plastic material, which are spread over one another like a web. The wires mimic the network aspects of the physical structure of the human brain.

Advances in nanowire networks can support the creation of many real-world applications, such as enhanced robotics or sensory devices that need to make quick decisions in unpredictable environments.

These nanowire networks are like synthetic neural networks in that the nanowires act like neurons, and the places where they connect to each other are analogous to synapses..

Zdenka Kuncic, Author and Senior Professor of Studies, School of Physics, University of Sydney

Kuncic added, “Instead of implementing some sort of machine learning task, in this study Dr. Loeffler has actually taken it one step further and tried to show that nanowire networks exhibit some sort of cognitive function..”

To analyze the capabilities of the nanowire network, the team used a common memory test often used in human psychology experiments called the N-Back task.

For one person, the N-Back task might involve recalling a particular cat picture from a series of cat pictures presented in sequence. An N-Back score of 7, averaged across people, indicates that the person has the potential to identify the same image that appears seven steps back.

When used in a network of nanowires, scientists discovered that it could “remember” a preferred endpoint in an electrical circuit seven steps back. This implies a score of 7 on the N-Back test.

What we’re doing here is manipulating the tip electrode voltage to force the path to change, rather than letting the network do its own thing. We force the path to go where we want it.

Dr. Alon Loeffler, School of Physics, University of Sydney

Loeffler continues, “When we deploy it, the memory has much higher accuracy and doesn’t really diminish over time, suggesting that we’ve found a way to strengthen the pathway to push it where we want it to go, and then the network remembers it..

Neuroscientists think that’s how the brain works, certain synaptic connections strengthen while others weaken, and that’s supposed to be how we remember things, how we learn, and so on.,” added Loeffler.

The researchers stated that when the nanowire network is continuously strengthened, it reaches a point where that reinforcement is no longer needed as the information is consolidated into memory.

Professor Kuncic stated, “It’s 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 it, otherwise it will fade over time..”

Professor Kuncic added, “One task demonstrated that nanowire networks could store up to seven items in memory at substantially higher than chance rates 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.



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