| Posted: May 01, 2017 |
Researchers build artificial synapse capable of autonomous learning
(Nanowerk News) 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.
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The results were published April 3 in the journal Nature Communications ("Learning through ferroelectric domain dynamics in solid-state synapses").
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| (a) Sketch of pre- and post-neurons connected by a synapse. The synaptic transmission is modulated by the causality (Δt) of neuron spikes. (b) Sketch of the ferroelectric memristor where a ferroelectric tunnel barrier of BiFeO3 (BFO) is sandwiched between a bottom electrode of (Ca,Ce)MnO3 (CCMO) and a top submicron pillar of Pt/Co. YAO stands for YAlO3. (c) Single-pulse hysteresis loop of the ferroelectric memristor displaying clear voltage thresholds. (d) Measurements of STDP in the ferroelectric memristor. Modulation of the device conductance (ΔG) as a function of the delay (Δt) between pre- and post-synaptic spikes. Seven data sets were collected on the same device showing the reproducibility of the effect. The total length of each pre- and post-synaptic spike is 600 ns. (© Nature Communications) (click on image to enlarge)
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“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.
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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.
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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.
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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.
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