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Time and rate dependent synaptic learning in neuro-mimicking resistive memories

Memristors have demonstrated immense potential as building blocks in future adaptive neuromorphic architectures. Recently, there has been focus on emulating specific synaptic functions of the mammalian nervous system by either tailoring the functional oxides or engineering the external programming h...

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Autores principales: Ahmed, Taimur, Walia, Sumeet, Mayes, Edwin L. H., Ramanathan, Rajesh, Bansal, Vipul, Bhaskaran, Madhu, Sriram, Sharath, Kavehei, Omid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6817848/
https://www.ncbi.nlm.nih.gov/pubmed/31659247
http://dx.doi.org/10.1038/s41598-019-51700-0
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author Ahmed, Taimur
Walia, Sumeet
Mayes, Edwin L. H.
Ramanathan, Rajesh
Bansal, Vipul
Bhaskaran, Madhu
Sriram, Sharath
Kavehei, Omid
author_facet Ahmed, Taimur
Walia, Sumeet
Mayes, Edwin L. H.
Ramanathan, Rajesh
Bansal, Vipul
Bhaskaran, Madhu
Sriram, Sharath
Kavehei, Omid
author_sort Ahmed, Taimur
collection PubMed
description Memristors have demonstrated immense potential as building blocks in future adaptive neuromorphic architectures. Recently, there has been focus on emulating specific synaptic functions of the mammalian nervous system by either tailoring the functional oxides or engineering the external programming hardware. However, high device-to-device variability in memristors induced by the electroforming process and complicated programming hardware are among the key challenges that hinder achieving biomimetic neuromorphic networks. Here, a simple hybrid complementary metal oxide semiconductor (CMOS)-memristor approach is reported to implement different synaptic learning rules by utilizing a CMOS-compatible memristor based on oxygen-deficient SrTiO(3-x) (STO(x)). The potential of such hybrid CMOS-memristor approach is demonstrated by successfully imitating time-dependent (pair and triplet spike-time-dependent-plasticity) and rate-dependent (Bienenstosk-Cooper-Munro) synaptic learning rules. Experimental results are benchmarked against in-vitro measurements from hippocampal and visual cortices with good agreement. The scalability of synaptic devices and their programming through a CMOS drive circuitry elaborates the potential of such an approach in realizing adaptive neuromorphic computation and networks.
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spelling pubmed-68178482019-11-01 Time and rate dependent synaptic learning in neuro-mimicking resistive memories Ahmed, Taimur Walia, Sumeet Mayes, Edwin L. H. Ramanathan, Rajesh Bansal, Vipul Bhaskaran, Madhu Sriram, Sharath Kavehei, Omid Sci Rep Article Memristors have demonstrated immense potential as building blocks in future adaptive neuromorphic architectures. Recently, there has been focus on emulating specific synaptic functions of the mammalian nervous system by either tailoring the functional oxides or engineering the external programming hardware. However, high device-to-device variability in memristors induced by the electroforming process and complicated programming hardware are among the key challenges that hinder achieving biomimetic neuromorphic networks. Here, a simple hybrid complementary metal oxide semiconductor (CMOS)-memristor approach is reported to implement different synaptic learning rules by utilizing a CMOS-compatible memristor based on oxygen-deficient SrTiO(3-x) (STO(x)). The potential of such hybrid CMOS-memristor approach is demonstrated by successfully imitating time-dependent (pair and triplet spike-time-dependent-plasticity) and rate-dependent (Bienenstosk-Cooper-Munro) synaptic learning rules. Experimental results are benchmarked against in-vitro measurements from hippocampal and visual cortices with good agreement. The scalability of synaptic devices and their programming through a CMOS drive circuitry elaborates the potential of such an approach in realizing adaptive neuromorphic computation and networks. Nature Publishing Group UK 2019-10-28 /pmc/articles/PMC6817848/ /pubmed/31659247 http://dx.doi.org/10.1038/s41598-019-51700-0 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Ahmed, Taimur
Walia, Sumeet
Mayes, Edwin L. H.
Ramanathan, Rajesh
Bansal, Vipul
Bhaskaran, Madhu
Sriram, Sharath
Kavehei, Omid
Time and rate dependent synaptic learning in neuro-mimicking resistive memories
title Time and rate dependent synaptic learning in neuro-mimicking resistive memories
title_full Time and rate dependent synaptic learning in neuro-mimicking resistive memories
title_fullStr Time and rate dependent synaptic learning in neuro-mimicking resistive memories
title_full_unstemmed Time and rate dependent synaptic learning in neuro-mimicking resistive memories
title_short Time and rate dependent synaptic learning in neuro-mimicking resistive memories
title_sort time and rate dependent synaptic learning in neuro-mimicking resistive memories
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6817848/
https://www.ncbi.nlm.nih.gov/pubmed/31659247
http://dx.doi.org/10.1038/s41598-019-51700-0
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