高清福利片

The research has been published in the journal , led by Dr Alon Loeffler, who received his PhD in the School of Physics, with collaborators in Japan.

鈥淚n this research we found higher-order cognitive function, which we normally associate with the human brain, can be emulated in non-biological hardware,鈥� Dr Loeffler said.

鈥淭his 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 signalling.

鈥淥ur 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.鈥�

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.

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

鈥淭his 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,鈥� senior author聽Professor Zdenka Kuncic, from the School of Physics, said.

鈥淚nstead 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.鈥�

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

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 recognise the same image that appeared seven steps back.

When applied to the nanowire network, the researchers found it could 鈥榬emember鈥� a desired endpoint in an electric circuit seven steps back, meaning a score of 7 in an N-Back test.

鈥淲hat 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,鈥� Dr Loeffler聽said.

鈥淲hen we implement that, its memory had much higher accuracy and didn鈥檛 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.

鈥淣euroscientists 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.鈥�

The researchers聽said 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.

鈥淚t's kind of like the difference between long-term memory and short-term memory in our brains,鈥� Professor Kuncic said.

鈥淚f 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.

鈥淥ne 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.鈥�

DOI:听10.1126/蝉肠颈补诲惫.补诲驳3289

Declaration: Professor Zdenka Kuncic is with Emergentia, Inc. The authors declare that they have no other competing interests.