Tags

Type your tag names separated by a space and hit enter

Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors.
Sci Rep 2016; 6:21331SR

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 dynamics in a simple system with a leaky-integrate-and-fire neuron with a random spike-train input, using a compact model of memristor plasticity, fitted for quantitatively correct description of our memristors.

Authors+Show Affiliations

Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, CA 93106, United States.Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, CA 93106, United States.Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, CA 93106, United States.Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY 11794, United States.Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, CA 93106, United States.

Pub Type(s)

Journal Article
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

26893175

Citation

Prezioso, M, et al. "Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors." Scientific Reports, vol. 6, 2016, p. 21331.
Prezioso M, Merrikh Bayat F, Hoskins B, et al. Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors. Sci Rep. 2016;6:21331.
Prezioso, M., Merrikh Bayat, F., Hoskins, B., Likharev, K., & Strukov, D. (2016). Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors. Scientific Reports, 6, p. 21331. doi:10.1038/srep21331.
Prezioso M, et al. Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors. Sci Rep. 2016 Feb 19;6:21331. PubMed PMID: 26893175.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors. AU - Prezioso,M, AU - Merrikh Bayat,F, AU - Hoskins,B, AU - Likharev,K, AU - Strukov,D, Y1 - 2016/02/19/ PY - 2015/12/29/received PY - 2016/01/18/accepted PY - 2016/2/20/entrez PY - 2016/2/20/pubmed PY - 2016/2/20/medline SP - 21331 EP - 21331 JF - Scientific reports JO - Sci Rep VL - 6 N2 - 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 dynamics in a simple system with a leaky-integrate-and-fire neuron with a random spike-train input, using a compact model of memristor plasticity, fitted for quantitatively correct description of our memristors. SN - 2045-2322 UR - https://www.unboundmedicine.com/medline/citation/26893175/Self_Adaptive_Spike_Time_Dependent_Plasticity_of_Metal_Oxide_Memristors_ L2 - http://dx.doi.org/10.1038/srep21331 DB - PRIME DP - Unbound Medicine ER -