Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors

Scientific Reports
M PreziosoD B Strukov

Abstract

Metal-oxide memristors have emerged as promising candidates for hardware implementation of artificial synapses - the key components of high-performance, analog neuromorphic networks - due to their excellent scaling prospects. Since some advanced cognitive tasks require spiking neuromorphic networks, which explicitly model individual neural pulses ("spikes") in biological neural systems, it is crucial for memristive synapses to support the spike-time-dependent plasticity (STDP). A major challenge for the STDP implementation is that, in contrast to some simplistic models of the plasticity, the elementary change of a synaptic weight in an artificial hardware synapse depends not only on the pre-synaptic and post-synaptic signals, but also on the initial weight (memristor's conductance) value. Here we experimentally demonstrate, for the first time, an STDP behavior that ensures self-adaptation of the average memristor conductance, making the plasticity stable, i.e. insensitive to the initial state of the devices. The experiments have been carried out with 200-nm Al2O3/TiO2-x memristors integrated into 12 × 12 crossbars. The experimentally observed self-adaptive STDP behavior has been complemented with numerical modeling of weight dy...Continue Reading

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Citations

Nov 9, 2016·Frontiers in Neuroscience·Erika CoviSabina Spiga
Mar 16, 2017·Nano Letters·F HartmannL Worschech
Apr 8, 2017·Scientific Reports·Zheng-Hua TanXin Guo
Dec 16, 2017·Scientific Reports·Vladimir KornijcukDoo Seok Jeong
Nov 14, 2018·Nanotechnology·Chul-Heung KimJong-Ho Lee
Nov 8, 2019·Nanotechnology·Daniele Ielmini, Stefano Ambrogio
Dec 2, 2018·Frontiers in Neuroinformatics·Vyacheslav Demin, Dmitry Nekhaev
Mar 7, 2017·Frontiers in Neuroscience·Alessandro ChiolerioSergio Bocchini
Jan 8, 2020·Materials·Valerio MiloDaniele Ielmini
Jul 28, 2019·Scientific Reports·Anton A MinnekhanovVictor V Erokhin
Nov 10, 2020·Advanced Materials·Jia-Qin YangSu-Ting Han

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