Summary: Researchers have developed an artificial synapse they say is capable of autonomous learning.
Source: University of Arkansas, Fayetteville.
Researchers from France and the University of Arkansas 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.
The results were published April 3 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.”
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 U of A 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.
About this neuroscience research article
Source: Robert Whitby – University of Arkansas, Fayetteville Image Source: NeuroscienceNews.com image is credited to Xu et al./Nature Communications. Original Research: Full open access research for “Learning through ferroelectric domain dynamics in solid-state synapses” by Sören Boyn, Julie Grollier, Gwendal Lecerf, Bin Xu, Nicolas Locatelli, Stéphane Fusil, Stéphanie Girod, Cécile Carrétéro, Karin Garcia, Stéphane Xavier, Jean Tomas, Laurent Bellaiche, Manuel Bibes, Agnès Barthélémy, Sylvain Saïghi & Vincent Garcia in Nature Communications. Published online April 3 2017 doi:10.1038/ncomms14736
Cite This NeuroscienceNews.com Article
[cbtabs][cbtab title=”MLA”]University of Arkansas, Fayetteville “Researchers Build Artificial Synapse Capable of Autonomous Learning.” NeuroscienceNews. NeuroscienceNews, 28 April 2017. <https://neurosciencenews.com/learning-artificial-synapse-6539/>.[/cbtab][cbtab title=”APA”]University of Arkansas, Fayetteville (2017, April 28). Researchers Build Artificial Synapse Capable of Autonomous Learning. NeuroscienceNew. Retrieved April 28, 2017 from https://neurosciencenews.com/learning-artificial-synapse-6539/[/cbtab][cbtab title=”Chicago”]University of Arkansas, Fayetteville “Researchers Build Artificial Synapse Capable of Autonomous Learning.” https://neurosciencenews.com/learning-artificial-synapse-6539/ (accessed April 28, 2017).[/cbtab][/cbtabs]
Learning through ferroelectric domain dynamics in solid-state synapses
In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.
“Learning through ferroelectric domain dynamics in solid-state synapses” by Sören Boyn, Julie Grollier, Gwendal Lecerf, Bin Xu, Nicolas Locatelli, Stéphane Fusil, Stéphanie Girod, Cécile Carrétéro, Karin Garcia, Stéphane Xavier, Jean Tomas, Laurent Bellaiche, Manuel Bibes, Agnès Barthélémy, Sylvain Saïghi & Vincent Garcia in Nature Communications. Published online April 3 2017 doi:10.1038/ncomms14736