Scientists Build Artificial Synapse Capable of Autonomous Learning

Wednesday, May 3, 2017

Artificial Intelligence

Researchers have created an artificial synapse capable of autonomous learning, a component of artificial intelligence. The discovery opens the door to building large networks that operate in ways similar to the human brain.

Computer scientists often now take inspiration from the functioning of the brain in order to design increasingly intelligent machines. This principle is already at work in information technology, in the form of the algorithms used for completing certain tasks, such as image recognition; this, for instance, is what Facebook uses to identify photos.

Now, scientists from France and the United States have created an artificial synapse capable of autonomous learning, a component of artificial intelligence. They have also developed a physical model that explains this learning capacity. This discovery opens the way to creating a network of synapses and hence intelligent systems requiring less time and energy.

The results were published in the journal Nature Communications.

“People are interested in building artificial brain networks in the future,” said Bin Xu, a research associate in the University of Arkansas Department of Physics. “This research is a fundamental advance.”

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The brain learns when synapses make connections among neurons. The connections vary in strength, with a strong connection correlating to a strong memory and improved learning. It is a concept called synaptic plasticity, and researchers see it as a model to advance machine learning.

A team of French scientists designed and built an artificial synapse, called a memristor, made of an ultrathin ferroelectric tunnel junction that can be tuned for conductivity by voltage pulses. The material is sandwiched between electrodes, and the variability in its conductivity determines whether a strong or weak connection is made between the electrodes.

Xu and Laurent Bellaiche, distinguished professor in the University of Arkansas physics department, helped by providing a microscopic insight of how the device functions, which will enable future researchers to create larger, more powerful, self-learning networks.

Memristors are not new, but until now their working principles have not been well understood. The study provided a clear explanation of the physical mechanism underlying the artificial synapse. The University of Arkansas researchers conducted computer simulations that clarified the switching mechanism in the ferroelectric tunnel junctions, backing up the measurements conducted by the French scientists.

Research focused on artificial synapses is being conducted at laboratories across the globe. So far, the function of these devices is still not entirely understood. The researchers involved in this project have succeeded, for the first time, in developing a physical model able to predict how they function. This understanding of the process will make it possible to create more complex systems, such as a series of artificial neurons interconnected by memristors.

"Our simulations therefore emphasize the importance of a precise knowledge of the memristor dynamics, and therefore of its accurate description on the basis of a physical model," conclude the researchers.

These results could pave the way toward low-power hardware implementations of millions or billions of reliable and predictable artificial synapses. These could function in deep neural networks, and in other in future brain-inspired computers.

SOURCE  University of Arkansas

By  33rd SquareEmbed


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