Quantum material-based neuristor ‘learns’ like human brain

Quantum material-based neuristor ‘learns’ like human brain

Technology News |
Researchers at UC San Diego's new Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C) say they have created a new nanoscale "neuristor" that mimics basic learning functions carried out by neurons in the human brain.
By Rich Pell


The researchers demonstrated how quantum materials – materials whose essential properties cannot be described in terms of semiclassical particles and low-level quantum mechanics – could be used to develop new devices that can “learn” to switch between functional states.

“The fact that we can make devices that function and are similar to neurons – but perhaps can even be better than the neurons that nature created – is quite surprising,” says Oleg Shpyrko, a professor in the Physics department at UC San Diego. “There are a whole class of tasks where our brains still outperform the traditional semiconductor approaches to computing. We use these brain-inspired approaches to come up with devices that can perform similar types of computation.”

While working to advance biologically inspired brain – or neuromorphic – computing systems, the researchers developed a device made of quantum material that could functionally learn with repeated stimuli. Blending the terms “neurons” and “transistors,” these devices are called “neuristors.”

With repeated practice inputs, neuristors can learn in the same way that humans figure out how to ride bikes, learn a new language, or play the piano. As additional stimuli are reinforced with a skill or task, the more human brains learn.

The key challenge to developing such brain-like devices, say the researchers, is to first develop artificial neurons, the cells that send and receive information across the brain and nervous system. Creating neuristors to work like neurons requires control of the nanoscale mechanisms of two basic types of quantum material functions known as volatile and persistent resistive switching.

Neuristors allowed the researchers to control this switching process, opening the door to neuron-like learning via repeated stimuli with minimal energy requirement.

“Improving their energy efficiency makes these neuristors approach the functionalities of neurons in the brain,” says UC San Diego Physics Professor Ivan Schuller, the director of Q-MEEN-C.

“The importance is,” says Shpyrko, “that for the first time we can take this neuristor device, which simulates behavior that we find in neurons, and we can train it. Through repeated stimuli the neuristor ‘reprograms’ itself to behave in a very different way than it was functioning before, establishing a persistent conductive pathway. This is an important step towards low-power approaches to neuromorphic computing.”

The researchers say they now plan to build multiple devices that can talk to each other, eventually ramping up to a neuristor network.

“Sometimes a group of people thinking together can be much more powerful than the sum of its parts, somewhat like brain storming,” says Shpyrko. “By allowing the neurons to talk to each other we can create a new phenomenon that could not be explained by a single device but really a network of devices.”

For more, see “Nanoscale Imaging and Control of Volatile and Non-Volatile Resistive Switching in VO2.”

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