New Use for Silver: Appears to Replicate Human Brain’s Thought & Memory

Astounding new research by five Physicists from The University of Sydney School of Physics in Sydney, Australia, and two scientists from Japan and the United States may have discovered a giant leap in developing a full function of Artificial Intelligence that works like the human brain.   

Unless you have been living under a rock for the last several years and haven’t been tracking, you understand the miracles achieved by science in generative artificial intelligence (AI) models such as ChatGPT and DALL-E and how they have made it possible to produce vast quantities of apparently human-like, high-quality creative content from a simple series of prompts.

ChatGPT and DALL-E have been proven highly capable – far outperforming human brains in big-data pattern recognition tasks in particular – current AI systems are not intelligent in the same way we are. AI systems aren’t structured like our brains and don’t learn the same way.

Another problem with AI systems is that they also use tremendous of amounts of energy and resources for training as compared to human learning (compared to our three-or-so meals a day). The ability of AI to adapt and function in dynamic, hard-to-predict, and noisy environments could be better in comparison to ours, and they lack the human-like interoperative ability and memory that humans use every day.

A new study published in Science Advances found that self-organizing networks of tiny silver wires appear to learn and remember in much the same way as the thinking hardware in our heads.

Imitating the Human Brain

The work published by this impressive team of physicists and scientists from Japan and the United States is part of a field of research called “neuromorphic, which aims to replicate the structure and functionality of biological neurons and synapses in non-biological systems.

According to Science Advances’ the research focuses on a system that uses a network of “nanowires” to mimic the neurons and synapses in the brain. These nanowires are tiny wires about one-thousandth the width of a human hair. They are made of a highly conductive metal, such as silver, typically coated in an insulating material like plastic.

Left: microscope image of silver nanowire networks from our Science Advances paper. Right: strengthened and pruned (weakened) pathways in nanowire networks.

According to the paper published by Science Advances to Nanowires, self-assemble to form a network structure similar to a biological neural network. Like neurons with an insulating membrane, each metal nanowire is coated with a thin insulating layer.

When we stimulate nanowires with electrical signals, ions migrate across the insulating layer and into a neighboring nanowire (much like neurotransmitters across synapses). As a result, we observe synapse-like electrical signaling in nanowire networks.

Learning and Memory

The paper published by Science Advances claims that…

“New work uses this nanowire system to explore the question of human-like intelligence. Central to the investigation are two features indicative of high-order cognitive function: learning and memory.

“Our study demonstrates we can selectively strengthen (and weaken) synaptic pathways in nanowire networks. This is similar to “supervised learning” in the brain. In this process, the output of synapses is compared to a desired result. Then the synapses are strengthened (if their output is close to the desired result) or pruned (if their output is not close to the desired result).”

“We expanded on this result by showing we could increase the amount of strengthening by “rewarding” or “punishing” the network. This process is inspired by “reinforcement learning” in the brain.”

“We also implemented a version of a test called the “n-back task” which is used to measure working memory in humans. It involves presenting a series of stimuli and comparing each new entry with one that occurred some number of steps (n) ago.”

“The network “remembered” previous signals for at least seven steps. Curiously, seven is often regarded as the average number of items humans can keep in working memory at one time.”

“When we used reinforcement learning, we saw dramatic improvements in the network’s memory performance.”

“In our nanowire networks, we found that the formation of synaptic pathways depends on how those synapses have been activated in the past. This is also the case for synapses in the brain, which neuroscientists call it “metaplasticity”. “

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