Las redes de nanocables aprenden y recuerdan como un cerebro humano

Artificial Intelligence Neural Network Concept Art

Los científicos han demostrado que las matrices de nanocables pueden exhibir una memoria a corto y largo plazo, similar al cerebro humano. Estas redes, compuestas por alambres de plata altamente conductivos recubiertos de plástico y dispuestos en un patrón similar a una malla, imitan la estructura física del cerebro humano. El equipo probó con éxito las capacidades de memoria de la matriz de nanocables mediante una tarea similar a los experimentos de psicología humana. Este avance en la nanotecnología sugiere que los sistemas de hardware no biológicos podrían potencialmente replicar el aprendizaje y la memoria similares al cerebro, y tener muchas aplicaciones en el mundo real, como mejorar la robótica y los dispositivos de detección en entornos impredecibles.

La inteligencia similar a la humana podría ser física

En un estudio innovador, un equipo internacional ha demostrado que las matrices de nanocables pueden imitar las funciones de memoria a corto y largo plazo del cerebro humano. Este avance allana el camino para reproducir el aprendizaje y la memoria similares al cerebro en sistemas no biológicos, con aplicaciones potenciales en robótica y dispositivos de detección.

Un equipo internacional dirigido por científicos de la Universidad de Sydney ha demostrado que las redes de nanocables pueden exhibir memoria tanto a corto como a largo plazo como el cerebro humano.

La investigación fue publicada hoy en la revista 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.

“Esta red de nanocables es como una red neuronal sintética porque los nanocables actúan como neuronas y los lugares donde se conectan son análogos a las sinapsis”, dijo la autora principal, la profesora Zdenka Kuncic, de la Escuela de física.

“En lugar de implementar algún tipo de[{” 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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