Low Power Stochastic Neurons From SiO<sub>2</sub>-Based Bilayer Conductive Bridge Memristors for Probabilistic Spiking Neural Network Applications—Part II: Modeling-Reference-Cited by-同舟云学术

Low Power Stochastic Neurons From SiO₂-Based Bilayer Conductive Bridge Memristors for Probabilistic Spiking Neural Network Applications—Part II: Modeling

Published:2022-05 Issue:5 Volume:69 Page:2368-2376
ISSN:0018-9383
Container-title:IEEE Transactions on Electron Devices
language:
Short-container-title:IEEE Trans. Electron Devices

Author:

Bousoulas P.¹^ORCID,Tsioustas C.¹^ORCID,Hadfield J.¹,Aslanidis V.¹,Limberopoulos S.¹^ORCID,Tsoukalas D.¹^ORCID

Affiliation:

1. School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Athens, Greece

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/16/9761777/09743837.pdf?arnumber=9743837

Reference35 articles.

1. A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis

2. Resistive RAM-Centric Computing: Design and Modeling Methodology

3. Tuning the analog synaptic properties of forming free SiO2 memristors by material engineering

4. Effects of heat dissipation on unipolar resistance switching in Pt∕NiO∕Pt capacitors

5. Phenomenological models of synaptic plasticity based on spike timing

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