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ナノワイヤ ネットワークは人間の脳のように学習し記憶する


人工知能ニューラル ネットワークのコンセプト アート

科学者たちは、ナノワイヤ ネットワークが人間の脳と同様に、短期記憶と長期記憶を示すことができることを実証しました。 これらのネットワークは、プラスチックで覆われ、メッシュ状のパターンに配置された導電性の高い銀線で構成されており、人間の脳の物理的構造を模倣しています。 チームは、人間の心理実験と同様のタスクを使用して、ナノワイヤ ネットワークの記憶機能をテストすることに成功しました。 ナノテクノロジーにおけるこのブレークスルーは、非生物学的ハードウェア システムが脳のような学習と記憶を複製できる可能性があることを示唆しており、予測不可能な環境でのロボット工学やセンサー デバイスの改善など、数多くの実世界での用途があります。


画期的な研究で、国際チームは、ナノワイヤ ネットワークが人間の脳の短期記憶機能と長期記憶機能を模倣できることを示しました。 このブレークスルーは、非生物学的システムで脳のような学習と記憶を再現する道を開き、ロボット工学やセンサー デバイスに応用できる可能性を秘めています。

科学者が率いる国際チーム[{” attribute=””>University of Sydney has demonstrated nanowire networks can exhibit both short- and long-term memory like the human brain.

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

Nanowire Network Pathways Changing and Strengthening

Photograph of nanowire network (left), network’s pathways changing and strengthening (right). Credit: Alon Loeffler

“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.

“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.”

Neural Network and Nanowire Network

Neural network (left) nanowire network (right). Credit: Loeffler et al.

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.

「このナノワイヤ ネットワークは、合成ニューラル ネットワークのようなものです。なぜなら、ナノワイヤはニューロンのように機能し、ナノワイヤが互いに接続する場所はシナプスに類似しているからです」と物理学科の上級著者 Zdenka Kuncic 教授は述べています。

「ある種の実装の代わりに[{” attribute=””>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.”

Zdenka Kuncic University of Sydney

Zdenka Kuncic. Credit: University of Sydney

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 recognize the same image that appeared seven steps back.

When applied to the nanowire network, the researchers found it could ‘remember’ a desired endpoint in an electric circuit seven steps back, meaning a score of 7 in 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. Loeffler said.

“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.

Alon Loeffler

Alon Loeffler. Credit: Alon Loeffler

“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.”

The researchers 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.

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

“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.

“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.”

Reference: “Neuromorphic learning, working memory, and metaplasticity in nanowire networks” by Alon Loeffler, Adrian Diaz-Alvarez, Ruomin Zhu, Natesh Ganesh, James M. Shine, Tomonobu Nakayama and Zdenka Kuncic, 21 April 2023, Science Advances.
DOI: 10.1126/sciadv.adg3289


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