Memristors—From In‐Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio‐Inspired Computing

Machine learning, particularly in the form of deep learning (DL), has driven most of the recent fundamental developments in artificial intelligence (AI). DL is based on computational models that are, to a certain extent, bio‐inspired, as they rely on networks of connected simple computing units oper...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Advanced intelligent systems Ročník 2; číslo 11
Hlavní autoři:	Mehonic, Adnan, Sebastian, Abu, Rajendran, Bipin, Simeone, Osvaldo, Vasilaki, Eleni, Kenyon, Anthony J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Weinheim John Wiley & Sons, Inc 01.11.2020 Wiley
Témata:	Algorithms Architecture Artificial intelligence Biomimetics CMOS Computer memory Deep learning Digital switching Electrodes in-memory computing Machine learning Memory Memristors Moore's law Neural networks neuromorphic systems Phase transitions power-efficient artificial intelligence Spiking spiking neural networks Technological change
ISSN:	2640-4567, 2640-4567
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Machine learning, particularly in the form of deep learning (DL), has driven most of the recent fundamental developments in artificial intelligence (AI). DL is based on computational models that are, to a certain extent, bio‐inspired, as they rely on networks of connected simple computing units operating in parallel. The success of DL is supported by three factors: availability of vast amounts of data, continuous growth in computing power, and algorithmic innovations. The approaching demise of Moore's law, and the consequent expected modest improvements in computing power that can be achieved by scaling, raises the question of whether the progress will be slowed or halted due to hardware limitations. This article reviews the case for a novel beyond‐complementary metal–oxide–semiconductor (CMOS) technology—memristors—as a potential solution for the implementation of power‐efficient in‐memory computing, DL accelerators, and spiking neural networks. Central themes are the reliance on non‐von‐Neumann computing architectures and the need for developing tailored learning and inference algorithms. To argue that lessons from biology can be useful in providing directions for further progress in AI, an example‐based reservoir computing is briefly discussed. At the end, speculation is given on the “big picture” view of future neuromorphic and brain‐inspired computing systems. Memristor technologies, with their remarkable diversity and richness of functional properties, can prove to be fundamental building blocks for the next generation of extraordinarily power‐efficient computing systems. Herein, it is discussed how memristors fit within the ever‐expanding field of hardware for artificial intelligence applications—from in‐memory computing, deep learning accelerators, and spiking neural networks to more bio‐inspired computing models.
AbstractList	Machine learning, particularly in the form of deep learning (DL), has driven most of the recent fundamental developments in artificial intelligence (AI). DL is based on computational models that are, to a certain extent, bio‐inspired, as they rely on networks of connected simple computing units operating in parallel. The success of DL is supported by three factors: availability of vast amounts of data, continuous growth in computing power, and algorithmic innovations. The approaching demise of Moore's law, and the consequent expected modest improvements in computing power that can be achieved by scaling, raises the question of whether the progress will be slowed or halted due to hardware limitations. This article reviews the case for a novel beyond‐complementary metal–oxide–semiconductor (CMOS) technology—memristors—as a potential solution for the implementation of power‐efficient in‐memory computing, DL accelerators, and spiking neural networks. Central themes are the reliance on non‐von‐Neumann computing architectures and the need for developing tailored learning and inference algorithms. To argue that lessons from biology can be useful in providing directions for further progress in AI, an example‐based reservoir computing is briefly discussed. At the end, speculation is given on the “big picture” view of future neuromorphic and brain‐inspired computing systems. Machine learning, particularly in the form of deep learning (DL), has driven most of the recent fundamental developments in artificial intelligence (AI). DL is based on computational models that are, to a certain extent, bio‐inspired, as they rely on networks of connected simple computing units operating in parallel. The success of DL is supported by three factors: availability of vast amounts of data, continuous growth in computing power, and algorithmic innovations. The approaching demise of Moore's law, and the consequent expected modest improvements in computing power that can be achieved by scaling, raises the question of whether the progress will be slowed or halted due to hardware limitations. This article reviews the case for a novel beyond‐complementary metal–oxide–semiconductor (CMOS) technology—memristors—as a potential solution for the implementation of power‐efficient in‐memory computing, DL accelerators, and spiking neural networks. Central themes are the reliance on non‐von‐Neumann computing architectures and the need for developing tailored learning and inference algorithms. To argue that lessons from biology can be useful in providing directions for further progress in AI, an example‐based reservoir computing is briefly discussed. At the end, speculation is given on the “big picture” view of future neuromorphic and brain‐inspired computing systems. Memristor technologies, with their remarkable diversity and richness of functional properties, can prove to be fundamental building blocks for the next generation of extraordinarily power‐efficient computing systems. Herein, it is discussed how memristors fit within the ever‐expanding field of hardware for artificial intelligence applications—from in‐memory computing, deep learning accelerators, and spiking neural networks to more bio‐inspired computing models.
Author	Vasilaki, Eleni Simeone, Osvaldo Rajendran, Bipin Mehonic, Adnan Kenyon, Anthony J. Sebastian, Abu
Author_xml	– sequence: 1 givenname: Adnan orcidid: 0000-0002-2476-5038 surname: Mehonic fullname: Mehonic, Adnan email: adnan.mehonic.09@ucl.ac.uk organization: UCL – sequence: 2 givenname: Abu surname: Sebastian fullname: Sebastian, Abu organization: IBM Research Europe – sequence: 3 givenname: Bipin surname: Rajendran fullname: Rajendran, Bipin organization: King's College London – sequence: 4 givenname: Osvaldo surname: Simeone fullname: Simeone, Osvaldo organization: King's College London – sequence: 5 givenname: Eleni surname: Vasilaki fullname: Vasilaki, Eleni organization: University of Sheffield – sequence: 6 givenname: Anthony J. surname: Kenyon fullname: Kenyon, Anthony J. organization: UCL
BookMark	eNqFkc9uEzEQxleoSJTSK2dLXJtge__ZxxAaulKAQ-HAyfI649bpZr2Mvapy6yNw6I2365PgJEAREsKX8Yy_3zcjz_PsqPc9ZNlLRqeMUv5au7CdcsppOqJ8kh3zqqCToqzqoz_uz7LTENZJwlnNKK-Ps-_vYYMuRI_h4e5-gX5Dmv7h7lsqe9ySud8MY3T91Rl5CzCQJWjsU0pmxkAHqKPz_RnR_YpcDu5m9_IBRtRdCvHW400g0ZN4DWQxxhGBeLsX-OQ-XDuzJ984nzo2fRgcwuqx54vsqdVdgNOf8ST7vDj_NL-YLD--a-az5cQUTJaTlsnCUC20ZUZapgXYKs8tQGkrU1pDc8tzKihvpaCybHOoIWd8xawEA5XJT7Lm4Lvyeq0GdBuNW-W1U_uCxyulMTrTgYK2LbVhZW2YKWwldCu4pFIYXlnQrU1erw5eA_qvI4So1n7EPo2vuOC8zgvGWFIVB5VBHwKCVcbF_V9G1K5TjKrdVtVuq-r3VhM2_Qv7New_AXkAbl0H2_-o1ay5_PLI_gAiyb1J
CitedBy_id	crossref_primary_10_3103_S0027134924701947 crossref_primary_10_3389_fncom_2022_859874 crossref_primary_10_1186_s40580_024_00432_7 crossref_primary_10_3389_fncom_2022_1037976 crossref_primary_10_1002_admt_202400585 crossref_primary_10_1016_j_nanoen_2025_110999 crossref_primary_10_1109_JMEMS_2025_3581048 crossref_primary_10_1088_2634_4386_acd4e2 crossref_primary_10_1103_PhysRevApplied_22_014041 crossref_primary_10_1038_s41598_021_04614_9 crossref_primary_10_1111_nyas_15256 crossref_primary_10_1002_aisy_202200028 crossref_primary_10_1002_jnm_70055 crossref_primary_10_1016_j_nanoen_2025_110871 crossref_primary_10_3390_nano15100724 crossref_primary_10_1109_TCSII_2022_3157789 crossref_primary_10_1002_aelm_202200702 crossref_primary_10_1063_5_0173662 crossref_primary_10_1063_5_0275293 crossref_primary_10_1134_S2635167621060100 crossref_primary_10_1557_s43577_022_00391_6 crossref_primary_10_1002_aelm_202300424 crossref_primary_10_1063_5_0148469 crossref_primary_10_1111_tgis_13264 crossref_primary_10_1016_j_nanoen_2024_109646 crossref_primary_10_1016_j_chaos_2020_110504 crossref_primary_10_3389_fnano_2024_1371386 crossref_primary_10_1063_5_0255043 crossref_primary_10_1109_TCSII_2022_3169416 crossref_primary_10_1038_s41598_023_45790_0 crossref_primary_10_1039_D4NH00508B crossref_primary_10_3389_fnins_2023_1253075 crossref_primary_10_1002_admt_202401048 crossref_primary_10_1038_s41928_022_00869_w crossref_primary_10_1088_2634_4386_ac970c crossref_primary_10_1088_2632_959X_ad2999 crossref_primary_10_1038_s41699_025_00556_2 crossref_primary_10_1038_s41598_024_53125_w crossref_primary_10_29026_oea_2025_240316 crossref_primary_10_1016_j_techfore_2023_122588 crossref_primary_10_1016_j_apsusc_2025_162305 crossref_primary_10_1021_acs_chemrev_4c00862 crossref_primary_10_1007_s40820_025_01891_1 crossref_primary_10_1039_D4NH00104D crossref_primary_10_1063_5_0286727 crossref_primary_10_1002_aisy_202300136 crossref_primary_10_1016_j_mattod_2023_09_003 crossref_primary_10_1002_adfm_202504017 crossref_primary_10_1109_TVLSI_2025_3552534 crossref_primary_10_3389_fncom_2025_1452203 crossref_primary_10_3390_electronics11162545 crossref_primary_10_3390_app132413309 crossref_primary_10_1109_JSEN_2022_3207912 crossref_primary_10_1002_adma_202210035 crossref_primary_10_1002_smll_202411596 crossref_primary_10_1016_j_nantod_2024_102184 crossref_primary_10_1038_s41467_024_47228_1 crossref_primary_10_1002_aelm_202200642 crossref_primary_10_1002_aelm_202300171 crossref_primary_10_3389_fmats_2022_813407 crossref_primary_10_1002_aelm_202200365 crossref_primary_10_3389_fnano_2025_1621554 crossref_primary_10_1016_j_eswa_2025_128406 crossref_primary_10_3390_nano13142082 crossref_primary_10_1002_aisy_202200047 crossref_primary_10_3390_agriengineering5010023 crossref_primary_10_1002_adma_202505642 crossref_primary_10_1063_5_0087001 crossref_primary_10_1088_2634_4386_acc683 crossref_primary_10_1109_MNANO_2022_3208723 crossref_primary_10_1038_s41467_023_42470_5 crossref_primary_10_1016_j_apmt_2024_102346 crossref_primary_10_1002_aelm_202400938 crossref_primary_10_1038_s42005_023_01352_4 crossref_primary_10_1039_D4NR04861J crossref_primary_10_1109_LED_2024_3355889 crossref_primary_10_1038_s44335_025_00035_3 crossref_primary_10_3390_jlpea12010014 crossref_primary_10_1038_s41598_025_85608_9 crossref_primary_10_1002_aelm_202200877 crossref_primary_10_1515_pac_2024_0242 crossref_primary_10_1002_cpe_7997 crossref_primary_10_1002_adem_202200439 crossref_primary_10_1063_5_0271898 crossref_primary_10_1109_TCSII_2021_3065932 crossref_primary_10_1088_1402_4896_acf89a crossref_primary_10_1002_adma_202301924 crossref_primary_10_1038_s41467_025_56595_2 crossref_primary_10_3390_electronics11101610 crossref_primary_10_1016_j_mssp_2025_109897 crossref_primary_10_1002_aisy_202500215 crossref_primary_10_1021_acs_chemrev_4c00049 crossref_primary_10_1109_TCAD_2021_3101464 crossref_primary_10_1063_5_0131593 crossref_primary_10_1063_5_0235922 crossref_primary_10_1109_JPROC_2023_3273520 crossref_primary_10_1088_1361_6641_ac4b8a crossref_primary_10_1002_adfm_202315058 crossref_primary_10_1109_TCSI_2021_3130938 crossref_primary_10_5194_jsss_11_233_2022 crossref_primary_10_1007_s43939_024_00077_7 crossref_primary_10_1063_5_0073056 crossref_primary_10_1109_ACCESS_2024_3387094 crossref_primary_10_1088_2634_4386_ad53f9 crossref_primary_10_1002_aisy_202200068 crossref_primary_10_1007_s11432_022_3601_8 crossref_primary_10_1063_5_0133044 crossref_primary_10_1002_smsc_202300139 crossref_primary_10_1088_2634_4386_ad409a crossref_primary_10_1109_TED_2025_3547291 crossref_primary_10_1088_2634_4386_ac156f crossref_primary_10_1109_LED_2023_3295337 crossref_primary_10_1002_aisy_202100054 crossref_primary_10_1002_aisy_202100175 crossref_primary_10_1002_aelm_202001258 crossref_primary_10_1002_adfm_202416309 crossref_primary_10_1021_acsami_5c03940 crossref_primary_10_1038_s41467_022_29727_1 crossref_primary_10_3390_jlpea12030044 crossref_primary_10_1002_adma_202416073 crossref_primary_10_1002_pssr_202300342 crossref_primary_10_1088_1361_6528_ac87b5 crossref_primary_10_1088_1751_8121_ace56f crossref_primary_10_3389_fnins_2022_959626 crossref_primary_10_1063_5_0162712 crossref_primary_10_1002_adma_202300576 crossref_primary_10_1038_s41578_022_00434_z crossref_primary_10_1002_adfm_202300343 crossref_primary_10_1109_TCAD_2023_3245543 crossref_primary_10_1039_D5MH00803D crossref_primary_10_1002_aisy_202400191 crossref_primary_10_1088_1361_6528_ad857f crossref_primary_10_1002_aisy_202401150 crossref_primary_10_3389_fncom_2023_1215824 crossref_primary_10_1016_j_mser_2025_101095 crossref_primary_10_1140_epjs_s11734_025_01615_x crossref_primary_10_1088_2631_8695_adfbbb crossref_primary_10_1038_s41586_021_04362_w crossref_primary_10_1088_2634_4386_ac4918 crossref_primary_10_1002_admt_202100144 crossref_primary_10_1002_aisy_202200127 crossref_primary_10_1016_j_trechm_2022_06_004 crossref_primary_10_1063_5_0179424 crossref_primary_10_1039_D4NR04865B crossref_primary_10_1002_aisy_202100273 crossref_primary_10_1016_j_mtnano_2023_100441 crossref_primary_10_1016_j_apsusc_2024_160681 crossref_primary_10_1109_JLT_2024_3433454 crossref_primary_10_1109_JPHOT_2022_3173559 crossref_primary_10_1016_j_sse_2025_109084 crossref_primary_10_3390_e26020126 crossref_primary_10_1016_j_chaos_2022_111813 crossref_primary_10_1016_j_jallcom_2025_178794 crossref_primary_10_1063_5_0073349 crossref_primary_10_1109_TED_2023_3317000 crossref_primary_10_1002_aisy_202400968 crossref_primary_10_1016_j_jksuci_2021_11_002 crossref_primary_10_1109_TED_2024_3482258 crossref_primary_10_1002_aelm_202201006 crossref_primary_10_1109_TED_2022_3227523 crossref_primary_10_3390_math13111878 crossref_primary_10_1007_s10470_024_02257_0 crossref_primary_10_23919_JSEE_2023_000018 crossref_primary_10_1002_advs_202304804 crossref_primary_10_1088_1361_6463_ad7155 crossref_primary_10_3389_fnins_2022_1050585 crossref_primary_10_1038_s41598_025_93159_2 crossref_primary_10_1039_D3RA06853F crossref_primary_10_1134_S2635167621060112 crossref_primary_10_3390_fractalfract8100605 crossref_primary_10_1016_j_sna_2024_115382 crossref_primary_10_18863_pgy_1539145 crossref_primary_10_1109_TNANO_2025_3565887 crossref_primary_10_3390_electronics11040619 crossref_primary_10_3390_chemistry7040133 crossref_primary_10_1002_smll_202505032
Cites_doi	10.1038/s41928-018-0039-7 10.1002/aelm.201700627 10.1109/CICC.2011.6055293 10.1088/1361-6528/aaa733 10.1038/s41928-018-0092-2 10.1038/nnano.2015.29 10.1038/s41467-018-04933-y 10.1038/s41598-017-09565-8 10.1021/acs.nanolett.5b00697 10.1038/nmat1614 10.1088/1361-6463/ab37b6 10.1038/s41467-020-16108-9 10.1109/IEDM.2015.7409716 10.1109/ICASSP.2018.8462410 10.3389/fnins.2016.00333 10.1109/MSSC.2019.2922889 10.1038/nnano.2016.70 10.1038/s41563-019-0291-x 10.1038/s41928-020-0410-3 10.1109/ESSDERC.2016.7599664 10.1002/aisy.201900189 10.1523/JNEUROSCI.4098-12.2013 10.1002/adma.201500039 10.1109/TCSI.2017.2705051 10.1038/s41928-017-0002-z 10.1088/0957-4484/27/36/365204 10.1162/089976602760407955 10.1109/IEDM.2012.6479018 10.1038/s41586-020-1942-4 10.3389/fnins.2020.00406 10.1109/TCSII.2014.2357292 10.18653/v1/P19-1355 10.3389/fnins.2016.00057 10.1038/ncomms3072 10.1109/IJCNN.2015.7280696 10.1002/9783527680870 10.1162/neco_a_01086 10.1038/s41467-017-02337-y 10.1016/j.neuron.2019.05.041 10.1021/nl201040y 10.1109/TNANO.2020.2982819 10.1002/aelm.201800143 10.1063/1.5009069 10.1147/JRD.2019.2947008 10.1109/JETCAS.2015.2435512 10.1002/aelm.201900198 10.1109/JPROC.2014.2310593 10.1109/TCT.1971.1083337 10.1038/nature06932 10.1109/SPAWC.2019.8815546 10.1109/JPROC.2017.2761740 10.1109/MSP.2019.2928127 10.1063/1.5129306 10.1038/s41598-018-31958-6 10.1039/C5NR04982B 10.1038/s41563-017-0001-5 10.3389/fnins.2018.00057 10.1109/DRC.2016.7548503 10.1038/s41565-018-0302-0 10.1038/nature24270 10.1038/nature08940 10.1038/nphys2566 10.1038/323533a0 10.1038/s41598-017-14240-z 10.1109/ICASSP.2019.8682960 10.1162/NECO_a_00200 10.1038/s41565-020-0655-z 10.1109/ISQED.2016.7479230 10.1002/9783527680870.ch21 10.1063/1.5042413 10.1126/science.1201938 10.1523/JNEUROSCI.18-24-10464.1998 10.1038/530144a 10.1039/C8FD00114F 10.1063/1.5143659 10.1109/TED.2020.2968223 10.3389/fnins.2019.00593 10.1109/JPROC.2018.2790840 10.1007/978-3-319-54313-0 10.1109/TED.2015.2439635 10.1038/s41467-017-01481-9 10.1088/1674-1056/ab75da 10.1145/3231589 10.1038/s41928-018-0054-8 10.1038/s41586-018-0180-5 10.1109/HPCA.2017.21 10.3389/fnins.2019.00855 10.1038/s41563-018-0110-9 10.1126/science.1254642 10.1073/pnas.1815682116 10.1109/JETCAS.2017.2776980 10.1007/s00339-011-6264-9 10.1109/LED.2016.2582859 10.1109/TCSI.2018.2846699 10.1016/j.mee.2017.04.033 10.1038/s42256-020-0162-9 10.1007/978-3-319-14367-5_13 10.1038/s41598-020-64878-5 10.1109/TED.2018.2865352 10.1038/s41565-019-0501-3 10.1038/s41928-017-0006-8 10.1109/MCAS.2016.2583673 10.23919/DATE.2019.8715020 10.1039/C8FD00118A 10.1109/IEDM.2018.8614558 10.1109/LED.2018.2831698 10.3389/fnins.2019.00095 10.1038/nnano.2012.240 10.1016/j.neucom.2019.10.104 10.1080/23746149.2016.1259585 10.1002/aelm.201900060 10.1088/1361-6528/aa8013 10.1109/IEDM.2013.6724692 10.1145/3123939.3124544 10.1109/DRC.2017.7999481 10.1109/MM.2011.89 10.1038/ncomms9181 10.1016/j.micpro.2019.01.009 10.1063/1.5129101 10.1145/3079856.3080246 10.1109/IJCNN.2017.7966434 10.1109/MSP.2019.2931595 10.1038/ncomms12611
ContentType	Journal Article
Copyright	2020 The Authors. Published by Wiley‐VCH GmbH 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: 2020 The Authors. Published by Wiley‐VCH GmbH – notice: 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	24P AAYXX CITATION 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS DOA
DOI	10.1002/aisy.202000085
DatabaseName	Wiley Online Library Open Access CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC ProQuest Central Technology Collection ProQuest One ProQuest Central Korea SciTech Premium Collection Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database Advanced Technologies & Aerospace Collection Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Publicly Available Content Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: 24P name: Wiley Online Library Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Architecture
EISSN	2640-4567
EndPage	n/a
ExternalDocumentID	oai_doaj_org_article_ebb5ac157c1c4f68ab829098c26feabf 10_1002_aisy_202000085 AISY202000085
Genre	article
GrantInformation_xml	– fundername: Engineering and Physical Sciences Research Council (EPSRC) – fundername: Royal Academy of Engineering – fundername: European Research Council (ERC) funderid: 682675; 725731
GroupedDBID	0R~ 1OC 24P AAFWJ AAHHS ACCFJ ACCMX ACXQS ADKYN ADZMN ADZOD AEEZP AEQDE AFKRA AIWBW AJBDE ALMA_UNASSIGNED_HOLDINGS ALUQN ARAPS ARCSS AVUZU BENPR BGLVJ CCPQU EBS EJD GROUPED_DOAJ HCIFZ IAO ITC M~E OK1 PIMPY WIN AAMMB AAYXX ADMLS AEFGJ AFFHD AFPKN AGXDD AIDQK AIDYY CITATION ICD PHGZM PHGZT PQGLB 8FE 8FG ABUWG AZQEC DWQXO P62 PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c4195-b194c0a8af1c9f1a8ef633fee5f6c5fc03f230802b98095b3e7e312d1f9ece6c3
IEDL.DBID	24P
ISICitedReferencesCount	222
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000669793800002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2640-4567
IngestDate	Tue Oct 14 18:22:06 EDT 2025 Fri Jul 25 03:24:50 EDT 2025 Sat Nov 29 07:21:02 EST 2025 Tue Nov 18 21:45:43 EST 2025 Wed Jan 22 16:33:23 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	11
Language	English
License	Attribution
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c4195-b194c0a8af1c9f1a8ef633fee5f6c5fc03f230802b98095b3e7e312d1f9ece6c3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-2476-5038
OpenAccessLink	https://onlinelibrary.wiley.com/doi/abs/10.1002%2Faisy.202000085
PQID	2822734111
PQPubID	5064933
PageCount	20
ParticipantIDs	doaj_primary_oai_doaj_org_article_ebb5ac157c1c4f68ab829098c26feabf proquest_journals_2822734111 crossref_citationtrail_10_1002_aisy_202000085 crossref_primary_10_1002_aisy_202000085 wiley_primary_10_1002_aisy_202000085_AISY202000085
PublicationCentury	2000
PublicationDate	November 2020
PublicationDateYYYYMMDD	2020-11-01
PublicationDate_xml	– month: 11 year: 2020 text: November 2020
PublicationDecade	2020
PublicationPlace	Weinheim
PublicationPlace_xml	– name: Weinheim
PublicationTitle	Advanced intelligent systems
PublicationYear	2020
Publisher	John Wiley & Sons, Inc Wiley
Publisher_xml	– name: John Wiley & Sons, Inc – name: Wiley
References	2017; 7 2002; 14 2017; 8 2017; 2 2013; 4 2019; 52 2019; 11 2018; 124 2019; 13 2010; 464 2019; 14 2020; 15 2020; 14 2019; 18 2020; 11 2020; 10 2017; 550 2017; 111 2013; 8 2014; 61 2012; 12 2016; 37 2013; 9 2020; 19 2020; 8 2020; 7 2018; 9 1998; 18 2018; 39 2018; 8 2020; 3 2020; 2 2019; 62 2018; 4 2001 2019; 63 2018; 1 1971; 18 2019; 67 2019; 116 2020; 577 2018; 30 2012; 24 2010; 4 2019; 7 2017; 64 2015; 15 2018; 29 2015; 6 2015; 5 2019; 5 2018; 106 2012 2011 2017; 28 2015; 10 2020; 380 2019; 36 2011; 31 2016; 10 2006; 5 2019; 103 2017; 178 2016; 16 2018; 65 2015; 7 2011; 332 2016; 11 2011; 102 2018; 17 2016; 7 1986; 323 2015; 27 2013; 33 2015; 62 2018; 558 2020 2016; 530 2019 2018 2017 2019; 213 2016 2015 2017; 18 2020; 67 2014 2013 2008; 453 2018; 12 2016; 27 2014; 345 2017; 105 2014; 102 2020; 29 e_1_2_11_70_1 e_1_2_11_93_1 e_1_2_11_32_1 e_1_2_11_55_1 e_1_2_11_36_1 e_1_2_11_51_1 e_1_2_11_74_1 e_1_2_11_97_1 e_1_2_11_13_1 e_1_2_11_118_1 e_1_2_11_29_1 e_1_2_11_125_1 e_1_2_11_4_1 e_1_2_11_106_1 e_1_2_11_48_1 e_1_2_11_121_1 e_1_2_11_102_1 e_1_2_11_81_1 e_1_2_11_20_1 e_1_2_11_66_1 e_1_2_11_47_1 e_1_2_11_89_1 e_1_2_11_24_1 e_1_2_11_62_1 e_1_2_11_129_1 e_1_2_11_8_1 e_1_2_11_85_1 e_1_2_11_17_1 Jaeger H. (e_1_2_11_120_1) 2001 e_1_2_11_59_1 e_1_2_11_113_1 Strubell E. (e_1_2_11_117_1) 2019 e_1_2_11_132_1 e_1_2_11_50_1 e_1_2_11_92_1 e_1_2_11_31_1 e_1_2_11_77_1 e_1_2_11_58_1 e_1_2_11_119_1 e_1_2_11_35_1 e_1_2_11_73_1 e_1_2_11_12_1 e_1_2_11_54_1 e_1_2_11_96_1 e_1_2_11_103_1 e_1_2_11_126_1 e_1_2_11_28_1 e_1_2_11_5_1 e_1_2_11_122_1 e_1_2_11_61_1 e_1_2_11_80_1 e_1_2_11_46_1 e_1_2_11_69_1 e_1_2_11_88_1 e_1_2_11_107_1 e_1_2_11_9_1 e_1_2_11_23_1 e_1_2_11_42_1 e_1_2_11_65_1 e_1_2_11_84_1 e_1_2_11_114_1 e_1_2_11_16_1 e_1_2_11_110_1 e_1_2_11_39_1 e_1_2_11_133_1 e_1_2_11_72_1 e_1_2_11_91_1 e_1_2_11_30_1 e_1_2_11_57_1 e_1_2_11_99_1 e_1_2_11_34_1 e_1_2_11_53_1 e_1_2_11_76_1 e_1_2_11_95_1 e_1_2_11_11_1 e_1_2_11_6_1 e_1_2_11_104_1 e_1_2_11_27_1 e_1_2_11_127_1 e_1_2_11_2_1 e_1_2_11_100_1 e_1_2_11_123_1 Shouval H. (e_1_2_11_78_1) 2010; 4 e_1_2_11_83_1 e_1_2_11_60_1 e_1_2_11_45_1 e_1_2_11_68_1 e_1_2_11_41_1 e_1_2_11_87_1 e_1_2_11_108_1 e_1_2_11_22_1 e_1_2_11_64_1 e_1_2_11_115_1 e_1_2_11_15_1 e_1_2_11_111_1 e_1_2_11_134_1 e_1_2_11_38_1 e_1_2_11_19_1 e_1_2_11_130_1 Hubara I. (e_1_2_11_43_1) 2017; 18 e_1_2_11_94_1 e_1_2_11_71_1 e_1_2_11_90_1 e_1_2_11_10_1 e_1_2_11_56_1 e_1_2_11_79_1 e_1_2_11_14_1 e_1_2_11_52_1 e_1_2_11_98_1 e_1_2_11_33_1 e_1_2_11_75_1 e_1_2_11_7_1 e_1_2_11_105_1 e_1_2_11_128_1 e_1_2_11_26_1 e_1_2_11_3_1 e_1_2_11_49_1 e_1_2_11_101_1 e_1_2_11_124_1 e_1_2_11_82_1 e_1_2_11_21_1 e_1_2_11_44_1 e_1_2_11_67_1 e_1_2_11_25_1 e_1_2_11_40_1 e_1_2_11_63_1 e_1_2_11_86_1 e_1_2_11_109_1 e_1_2_11_18_1 e_1_2_11_116_1 e_1_2_11_37_1 e_1_2_11_135_1 e_1_2_11_112_1 e_1_2_11_131_1
References_xml	– start-page: 1 year: 2016 end-page: 2 – start-page: 486 year: 2019 end-page: 491 – volume: 29 start-page: 115203 year: 2018 publication-title: Nanotechnology – volume: 4 start-page: 2072 year: 2013 publication-title: Nat. Commun. – volume: 453 start-page: 80 year: 2008 publication-title: Nature – volume: 30 start-page: 1514 year: 2018 publication-title: Neural Comput. – volume: 36 start-page: 51 year: 2019 publication-title: IEEE Signal Process. Mag. – volume: 14 start-page: 35 year: 2019 publication-title: Nature Nanotechnol. – volume: 4 start-page: 1700627 year: 2018 publication-title: Adv. Electron. Mater. – volume: 8 start-page: 2204 year: 2017 publication-title: Nat. Commun. – volume: 1 start-page: 246 year: 2018 publication-title: Nat. Electron. – volume: 102 start-page: 765 year: 2011 publication-title: Appl. Phys. A – volume: 8 start-page: 13 year: 2013 publication-title: Nat. Nanotechnol. – volume: 102 start-page: 860 year: 2014 publication-title: Proc. IEEE – volume: 464 start-page: 873 year: 2010 publication-title: Nature – volume: 17 start-page: 335 year: 2018 publication-title: Nat. Mater – start-page: 273 year: 2017 end-page: 287 – volume: 65 start-page: 4304 year: 2018 publication-title: IEEE Trans. Electron Devices – volume: 61 start-page: 895 year: 2014 publication-title: IEEE Trans. Circuits Syst. II – start-page: 1 year: 2019 end-page: 5 – start-page: 2986 year: 2018 end-page: 2990 – volume: 63 start-page: 1 year: 2019 publication-title: IBM J. Res. Dev. – volume: 28 start-page: 395202 year: 2017 publication-title: Nanotechnology – volume: 5 start-page: 1900198 year: 2019 publication-title: Adv. Electron. Mater. – volume: 10 start-page: 333 year: 2016 publication-title: Front. Neurosci. – volume: 9 start-page: 2514 year: 2018 publication-title: Nat. Commun. – start-page: 374 year: 2016 end-page: 379 – volume: 7 start-page: 011305 year: 2020 publication-title: Appl. Phys. Rev. – volume: 9 start-page: 200 year: 2013 publication-title: Nat. Phys. – volume: 5 start-page: 1900060 year: 2019 publication-title: Adv. Electron. Mater. – year: 2019 – volume: 16 start-page: 15 year: 2016 publication-title: IEEE Circuits Syst. Mag. – volume: 550 start-page: 354 year: 2017 publication-title: Nature – start-page: 373 year: 2016 end-page: 376 – volume: 18 start-page: 309 year: 2019 publication-title: Nat. Mater. – volume: 62 start-page: 110 year: 2019 publication-title: Commun. ACM – volume: 5 start-page: 312 year: 2006 publication-title: Nat. Mater – volume: 7 start-page: 18030 year: 2015 publication-title: Nanoscale – volume: 18 start-page: 6869 year: 2017 publication-title: J. Mach. Learn. Res. – start-page: 481 year: 2017 end-page: 492 – start-page: 329 year: 2011 end-page: 332 – volume: 12 start-page: 2179 year: 2012 publication-title: Nano Lett. – volume: 19 start-page: 328 year: 2020 publication-title: IEEE Trans. Nanotechnol. – volume: 213 start-page: 151 year: 2019 publication-title: Faraday Discuss. – volume: 8 start-page: 010401 year: 2020 publication-title: APL Mater. – year: 2016 – volume: 124 start-page: 111101 year: 2018 publication-title: J. Appl. Phys. – volume: 18 start-page: 507 year: 1971 publication-title: IEEE Trans. Circuit Theory – volume: 2 start-page: 89 year: 2017 publication-title: Adv. Phys. X – volume: 27 start-page: 2797 year: 2015 publication-title: Adv. Mater. – volume: 17 start-page: 681 year: 2018 publication-title: Nat. Mater – volume: 323 start-page: 533 year: 1986 publication-title: Nature – start-page: 1 year: 2011 end-page: 4 – start-page: 1 year: 2017 end-page: 12 – volume: 10 start-page: 8080 year: 2020 publication-title: Sci. Rep. – volume: 14 start-page: 2531 year: 2002 publication-title: Neural Comput. – volume: 14 start-page: 776 year: 2019 publication-title: Nat. Nanotechnol. – volume: 11 start-page: 43 year: 2019 publication-title: IEEE Solid-State Circuits Mag. – volume: 11 start-page: 693 year: 2016 publication-title: Nat. Nanotechnol. – volume: 65 start-page: 4258 year: 2018 publication-title: IEEE Trans. Circuits Syst. I – volume: 67 start-page: 1340 year: 2020 publication-title: IEEE Trans. Electron Devices – volume: 105 start-page: 2295 year: 2017 publication-title: Proc. IEEE – volume: 345 start-page: 668 year: 2014 publication-title: Science – volume: 380 start-page: 67 year: 2020 publication-title: Neurocomputing – start-page: 27.7.1 year: 2018 end-page: 27.7.4 – volume: 558 start-page: 60 year: 2018 publication-title: Nature – volume: 15 start-page: 2203 year: 2015 publication-title: Nano Lett. – volume: 530 start-page: 144 year: 2016 publication-title: Nature – start-page: 17.1.1 year: 2015 end-page: 17.1.4 – volume: 27 start-page: 365204 year: 2016 publication-title: Nanotechnology – volume: 111 start-page: 233502 year: 2017 publication-title: Appl. Phys. Lett. – start-page: 1 year: 2015 end-page: 8 – volume: 18 start-page: 10464 year: 1998 publication-title: J. Neurosci. – volume: 8 start-page: 1115 year: 2017 publication-title: Nat. Commun. – volume: 7 start-page: 12611 year: 2016 publication-title: Nat. Commun. – year: 2001 – start-page: 25.6.1 year: 2013 end-page: 25.6.4 – start-page: 3382 year: 2019 end-page: 3386 – volume: 13 start-page: 593 year: 2019 publication-title: Front. Neurosci. – volume: 577 start-page: 641 year: 2020 publication-title: Nature – volume: 8 start-page: 102 year: 2018 publication-title: IEEE J. Emerg. Sel. Topics Circuits Syst. – volume: 2 start-page: 1900189 year: 2020 publication-title: Adv. Intell. Syst. – start-page: 401 year: 2015 end-page: 428 – volume: 2 start-page: 155 year: 2020 publication-title: Nat. Mach. Intell. – start-page: 3645 year: 2019 end-page: 3650 – start-page: 4557 year: 2017 end-page: 4563 – volume: 213 start-page: 487 year: 2019 publication-title: Faraday Discuss. – volume: 1 start-page: 22 year: 2018 publication-title: Nat. Electron. – volume: 64 start-page: 2508 year: 2017 publication-title: IEEE Trans. Circuits Syst. I – start-page: 597 year: 2016 end-page: 622 – volume: 116 start-page: 4123 year: 2019 publication-title: Proc. Natl. Acad. Sci. USA – start-page: 35 year: 2018 end-page: 36 – volume: 7 start-page: 110903 year: 2019 publication-title: APL Mater. – volume: 29 start-page: 048401 year: 2020 publication-title: Chinese Phys. B – volume: 39 start-page: 819 year: 2018 publication-title: IEEE Electron Device Lett. – start-page: 1 year: 2018 end-page: 5 – volume: 1 start-page: 197 year: 2018 publication-title: Nat. Electron. – volume: 10 start-page: 57 year: 2016 publication-title: Front. Neurosci. – volume: 14 start-page: 406 year: 2020 publication-title: Front. Neurosci. – volume: 5 start-page: 242 year: 2015 publication-title: IEEE J. Emerg. Sel. Topics Circuits Syst. – volume: 106 start-page: 260 year: 2018 publication-title: Proc. IEEE – volume: 10 start-page: 191 year: 2015 publication-title: Nat. Nanotechnol. – volume: 4 start-page: 19 year: 2010 publication-title: Front. Comput. Neurosci. – volume: 24 start-page: 104 year: 2012 publication-title: Neural Comput. – volume: 5 start-page: 1800143 year: 2019 publication-title: Adv. Electron. Mater. – volume: 13 start-page: 95 year: 2019 publication-title: Front. Neurosci. – volume: 13 start-page: 855 year: 2019 publication-title: Front. Neurosci. – start-page: 10.4.1 year: 2012 end-page: 10.4.4 – volume: 52 start-page: 443002 year: 2019 publication-title: J. Phys. D: Appl. Phys. – volume: 3 start-page: 327 year: 2020 publication-title: Nat. Electron. – volume: 7 start-page: 9274 year: 2017 publication-title: Sci. Rep. – volume: 8 start-page: 13727 year: 2018 publication-title: Sci. Rep. – volume: 31 start-page: 7 year: 2011 publication-title: IEEE Micro. – volume: 178 start-page: 98 year: 2017 publication-title: Microelectron. Eng. – volume: 36 start-page: 29 year: 2019 publication-title: IEEE Signal Process. Mag. – volume: 11 start-page: 2473 year: 2020 publication-title: Nat. Commun. – volume: 6 start-page: 8181 year: 2015 publication-title: Nat. Commun. – volume: 33 start-page: 9565 year: 2013 publication-title: J. Neurosci. – volume: 103 start-page: 563 year: 2019 publication-title: Neuron – volume: 1 start-page: 333 year: 2018 publication-title: Nat. Electron. – volume: 62 start-page: 3498 year: 2015 publication-title: IEEE Trans. Electron Devices – volume: 332 start-page: 568 year: 2011 publication-title: Science – start-page: 1 year: 2014 end-page: 2 – volume: 15 start-page: 529 year: 2020 publication-title: Nat. Nanotechnol. – volume: 12 start-page: 57 year: 2018 publication-title: Front. Neurosci. – volume: 7 start-page: 14101 year: 2017 publication-title: Sci. Rep. – year: 2020 – volume: 67 start-page: 28 year: 2019 publication-title: Microprocess. Microsyst. – volume: 37 start-page: 994 year: 2016 publication-title: IEEE Electron Device Lett. – year: 2017 – volume: 1 start-page: 52 year: 2018 publication-title: Nat. Electron. – ident: e_1_2_11_35_1 doi: 10.1038/s41928-018-0039-7 – ident: e_1_2_11_42_1 doi: 10.1002/aelm.201700627 – ident: e_1_2_11_55_1 – ident: e_1_2_11_80_1 doi: 10.1109/CICC.2011.6055293 – ident: e_1_2_11_91_1 doi: 10.1088/1361-6528/aaa733 – ident: e_1_2_11_17_1 doi: 10.1038/s41928-018-0092-2 – ident: e_1_2_11_22_1 doi: 10.1038/nnano.2015.29 – ident: e_1_2_11_61_1 doi: 10.1038/s41467-018-04933-y – ident: e_1_2_11_95_1 doi: 10.1038/s41598-017-09565-8 – ident: e_1_2_11_81_1 doi: 10.1021/acs.nanolett.5b00697 – ident: e_1_2_11_7_1 doi: 10.1038/nmat1614 – ident: e_1_2_11_38_1 – ident: e_1_2_11_99_1 – ident: e_1_2_11_23_1 doi: 10.1088/1361-6463/ab37b6 – ident: e_1_2_11_40_1 doi: 10.1038/s41467-020-16108-9 – ident: e_1_2_11_83_1 doi: 10.1109/IEDM.2015.7409716 – ident: e_1_2_11_114_1 doi: 10.1109/ICASSP.2018.8462410 – ident: e_1_2_11_49_1 doi: 10.3389/fnins.2016.00333 – ident: e_1_2_11_53_1 doi: 10.1109/MSSC.2019.2922889 – ident: e_1_2_11_71_1 doi: 10.1038/nnano.2016.70 – ident: e_1_2_11_118_1 doi: 10.1038/s41563-019-0291-x – ident: e_1_2_11_31_1 doi: 10.1038/s41928-020-0410-3 – ident: e_1_2_11_65_1 doi: 10.1109/ESSDERC.2016.7599664 – ident: e_1_2_11_119_1 doi: 10.1002/aisy.201900189 – ident: e_1_2_11_110_1 doi: 10.1523/JNEUROSCI.4098-12.2013 – ident: e_1_2_11_130_1 doi: 10.1002/adma.201500039 – ident: e_1_2_11_30_1 doi: 10.1109/TCSI.2017.2705051 – ident: e_1_2_11_92_1 doi: 10.1038/s41928-017-0002-z – ident: e_1_2_11_101_1 doi: 10.1088/0957-4484/27/36/365204 – ident: e_1_2_11_121_1 doi: 10.1162/089976602760407955 – ident: e_1_2_11_66_1 doi: 10.1109/IEDM.2012.6479018 – ident: e_1_2_11_67_1 doi: 10.1038/s41586-020-1942-4 – ident: e_1_2_11_45_1 doi: 10.3389/fnins.2020.00406 – ident: e_1_2_11_27_1 doi: 10.1109/TCSII.2014.2357292 – volume: 4 start-page: 19 year: 2010 ident: e_1_2_11_78_1 publication-title: Front. Comput. Neurosci. – start-page: 3645 volume-title: Proc. of the 57th Annu. Meeting of the Association for Computational Linguistics year: 2019 ident: e_1_2_11_117_1 doi: 10.18653/v1/P19-1355 – ident: e_1_2_11_72_1 doi: 10.3389/fnins.2016.00057 – ident: e_1_2_11_48_1 doi: 10.1038/ncomms3072 – ident: e_1_2_11_74_1 doi: 10.1109/IJCNN.2015.7280696 – ident: e_1_2_11_5_1 doi: 10.1002/9783527680870 – ident: e_1_2_11_87_1 doi: 10.1162/neco_a_01086 – ident: e_1_2_11_124_1 doi: 10.1038/s41467-017-02337-y – ident: e_1_2_11_79_1 doi: 10.1016/j.neuron.2019.05.041 – ident: e_1_2_11_69_1 doi: 10.1021/nl201040y – ident: e_1_2_11_108_1 doi: 10.1109/TNANO.2020.2982819 – ident: e_1_2_11_126_1 doi: 10.1002/aelm.201800143 – ident: e_1_2_11_131_1 doi: 10.1063/1.5009069 – ident: e_1_2_11_47_1 doi: 10.1147/JRD.2019.2947008 – ident: e_1_2_11_105_1 doi: 10.1109/JETCAS.2015.2435512 – ident: e_1_2_11_33_1 doi: 10.1002/aelm.201900198 – ident: e_1_2_11_103_1 doi: 10.1109/JPROC.2014.2310593 – ident: e_1_2_11_9_1 doi: 10.1109/TCT.1971.1083337 – ident: e_1_2_11_6_1 doi: 10.1038/nature06932 – ident: e_1_2_11_111_1 doi: 10.1109/SPAWC.2019.8815546 – ident: e_1_2_11_4_1 doi: 10.1109/JPROC.2017.2761740 – ident: e_1_2_11_113_1 doi: 10.1109/MSP.2019.2928127 – ident: e_1_2_11_128_1 doi: 10.1063/1.5129306 – ident: e_1_2_11_133_1 doi: 10.1038/s41598-018-31958-6 – ident: e_1_2_11_58_1 doi: 10.1039/C5NR04982B – ident: e_1_2_11_60_1 doi: 10.1038/s41563-017-0001-5 – ident: e_1_2_11_82_1 doi: 10.3389/fnins.2018.00057 – ident: e_1_2_11_70_1 doi: 10.1109/DRC.2016.7548503 – ident: e_1_2_11_57_1 doi: 10.1038/s41565-018-0302-0 – ident: e_1_2_11_116_1 doi: 10.1038/nature24270 – ident: e_1_2_11_25_1 doi: 10.1038/nature08940 – ident: e_1_2_11_19_1 doi: 10.1038/nphys2566 – ident: e_1_2_11_115_1 doi: 10.1038/323533a0 – ident: e_1_2_11_34_1 doi: 10.1038/s41598-017-14240-z – ident: e_1_2_11_112_1 doi: 10.1109/ICASSP.2019.8682960 – ident: e_1_2_11_123_1 doi: 10.1162/NECO_a_00200 – volume: 18 start-page: 6869 year: 2017 ident: e_1_2_11_43_1 publication-title: J. Mach. Learn. Res. – ident: e_1_2_11_18_1 doi: 10.1038/s41565-020-0655-z – ident: e_1_2_11_98_1 doi: 10.1109/ISQED.2016.7479230 – ident: e_1_2_11_90_1 doi: 10.1002/9783527680870.ch21 – ident: e_1_2_11_24_1 doi: 10.1063/1.5042413 – ident: e_1_2_11_32_1 – ident: e_1_2_11_54_1 doi: 10.1126/science.1201938 – ident: e_1_2_11_125_1 – ident: e_1_2_11_77_1 doi: 10.1523/JNEUROSCI.18-24-10464.1998 – ident: e_1_2_11_3_1 doi: 10.1038/530144a – ident: e_1_2_11_109_1 – ident: e_1_2_11_104_1 doi: 10.1039/C8FD00114F – ident: e_1_2_11_135_1 doi: 10.1063/1.5143659 – ident: e_1_2_11_107_1 doi: 10.1109/TED.2020.2968223 – ident: e_1_2_11_41_1 doi: 10.3389/fnins.2019.00593 – ident: e_1_2_11_64_1 doi: 10.1109/JPROC.2018.2790840 – ident: e_1_2_11_11_1 doi: 10.1007/978-3-319-54313-0 – ident: e_1_2_11_39_1 doi: 10.1109/TED.2015.2439635 – ident: e_1_2_11_15_1 doi: 10.1038/s41467-017-01481-9 – ident: e_1_2_11_132_1 doi: 10.1088/1674-1056/ab75da – ident: e_1_2_11_127_1 doi: 10.1145/3231589 – ident: e_1_2_11_44_1 doi: 10.1038/s41928-018-0054-8 – ident: e_1_2_11_50_1 doi: 10.1038/s41586-018-0180-5 – ident: e_1_2_11_52_1 doi: 10.1109/HPCA.2017.21 – ident: e_1_2_11_85_1 doi: 10.3389/fnins.2019.00855 – ident: e_1_2_11_56_1 doi: 10.1038/s41563-018-0110-9 – ident: e_1_2_11_68_1 doi: 10.1126/science.1254642 – ident: e_1_2_11_20_1 doi: 10.1073/pnas.1815682116 – ident: e_1_2_11_97_1 doi: 10.1109/JETCAS.2017.2776980 – ident: e_1_2_11_21_1 doi: 10.1007/s00339-011-6264-9 – ident: e_1_2_11_102_1 doi: 10.1109/LED.2016.2582859 – ident: e_1_2_11_28_1 doi: 10.1109/TCSI.2018.2846699 – ident: e_1_2_11_96_1 doi: 10.1016/j.mee.2017.04.033 – ident: e_1_2_11_122_1 doi: 10.1038/s42256-020-0162-9 – ident: e_1_2_11_10_1 doi: 10.1007/978-3-319-14367-5_13 – ident: e_1_2_11_89_1 doi: 10.1038/s41598-020-64878-5 – ident: e_1_2_11_37_1 doi: 10.1109/TED.2018.2865352 – ident: e_1_2_11_129_1 doi: 10.1038/s41565-019-0501-3 – ident: e_1_2_11_2_1 – ident: e_1_2_11_8_1 doi: 10.1038/s41928-017-0006-8 – ident: e_1_2_11_26_1 doi: 10.1109/MCAS.2016.2583673 – ident: e_1_2_11_29_1 doi: 10.23919/DATE.2019.8715020 – ident: e_1_2_11_94_1 doi: 10.1039/C8FD00118A – ident: e_1_2_11_63_1 doi: 10.1109/IEDM.2018.8614558 – ident: e_1_2_11_93_1 doi: 10.1109/LED.2018.2831698 – ident: e_1_2_11_75_1 doi: 10.3389/fnins.2019.00095 – ident: e_1_2_11_16_1 doi: 10.1038/nnano.2012.240 – ident: e_1_2_11_86_1 doi: 10.1016/j.neucom.2019.10.104 – ident: e_1_2_11_36_1 doi: 10.1080/23746149.2016.1259585 – ident: e_1_2_11_76_1 doi: 10.1002/aelm.201900060 – ident: e_1_2_11_59_1 doi: 10.1088/1361-6528/aa8013 – ident: e_1_2_11_106_1 – ident: e_1_2_11_100_1 doi: 10.1109/IEDM.2013.6724692 – ident: e_1_2_11_51_1 doi: 10.1145/3123939.3124544 – ident: e_1_2_11_46_1 doi: 10.1109/DRC.2017.7999481 – ident: e_1_2_11_13_1 doi: 10.1109/MM.2011.89 – ident: e_1_2_11_62_1 doi: 10.1038/ncomms9181 – ident: e_1_2_11_12_1 doi: 10.1016/j.micpro.2019.01.009 – ident: e_1_2_11_134_1 doi: 10.1063/1.5129101 – ident: e_1_2_11_14_1 doi: 10.1145/3079856.3080246 – ident: e_1_2_11_73_1 doi: 10.1109/IJCNN.2017.7966434 – volume-title: The “ECHO STATE” Approach to Analysing and Training Recurrent Neural Networks year: 2001 ident: e_1_2_11_120_1 – ident: e_1_2_11_88_1 doi: 10.1109/MSP.2019.2931595 – ident: e_1_2_11_84_1 doi: 10.1038/ncomms12611
SSID	ssj0002171027
Score	2.5632448
Snippet	Machine learning, particularly in the form of deep learning (DL), has driven most of the recent fundamental developments in artificial intelligence (AI). DL is...
SourceID	doaj proquest crossref wiley
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
SubjectTerms	Algorithms Architecture Artificial intelligence Biomimetics CMOS Computer memory Deep learning Digital switching Electrodes in-memory computing Machine learning Memory Memristors Moore's law Neural networks neuromorphic systems Phase transitions power-efficient artificial intelligence Spiking spiking neural networks Technological change
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NTtwwELYQ4tBL1YpW3ZYiH5C4EBE7ieMcF8qKlWBVCVrRk2XPjttIJVlttpW48QgcuPXteBL8k90uh4pLj4kmM5ZnMj_x5BtC9vLCaCO0TArDIcmB5YnmwiSpRMQcGVYBp_vrWTmZyKur6vPaqC_fExbhgePGHaIxhQZWlMAgt0Jq44_-KglcWNTGeu-bltVaMeV9sEu0XeQslyiNKT_UdXfjykEeW7OeRKEA1v8kw1zPU0OgGb0iL_sMkQ7jyl6TDWy2yZ9zvA4oAPPu4fZ-NG-v6bh5uL07942yNzTOZnBR6IB-QpzRHjX1Ox0CuLgStXxAdTOlF7Pafx2nHpXDiZnENvCOLlrqckE6ChgjtLWBoHXcZz9qCE8e1a2TOG782TxO_8p8Q76MTi6PT5N-rkICuR_NaFiVQ6qltgwqy7REK7LMIhZWQGEhzawrTGTKTSVdBmYyLDFjfMpshYACsrdks2kbfEeola58SXXKrHXeIMuNcKxtKSyHKdeAA5Is91lBDzruZ1_8VBEumSuvF7XSy4Dsr-hnEW7jn5RHXm0rKg-THW4441G98ajnjGdAdpZKV_272ynfWFu64M7YgPBgCM8sRQ3HF99WV-__x8I-kBeeYfzpcYdsLua_8CPZgt-LupvvBlt_BB3iCT8 priority: 102 providerName: Directory of Open Access Journals – databaseName: Advanced Technologies & Aerospace Database dbid: P5Z link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NbtQwELagcEBI_CMWCvIBiUutxs6fc0JbYMVKdFWpgAqXyJ6MSySahGRB6o2HQOL9eBJsx5u2B-DAMdHEtuTxzHhm8n2EPE1SrXSmJEu1AJYAT5gSmWaRRMQEORYep_v9m3y1kkdHxUFIuA2hrXJjE72hrlpwOfJd1-6YW5PL-fPuC3OsUa66Gig0LpMrDiXBUTccpB-nHIsNt63_zDdYjZHYVfVwai-FYmzQuuCLPGT_hTjzfLTq3c3i5v8u9Ba5EQJNOh814za5hM0dcn1-rm5wl_zcxxOPLdAPv77_WPTtCV02bN81357Ske_BerYd-hKxowGJ9ZjOAayvGjVnh6qmoodd7TLu1CF92DlXY2v5QNcttfElXXjcEtoaL9Da0btPNfgv9-qWLRtX7cfqbMZ75N3i1dsXr1lgamCQOLJHzYsEIiWV4VAYriSaLI4NYmoySA1EsbFXHRkJXUgb0-kYc4y5qLgpEDCD-D7ZatoGHxBqpL0QRSrixlj7Eic6s0ObPDMCKqEAZ4Rt9qyEAGPu2DQ-lyMAsyjdHpfTHs_Is0m-GwE8_ii551RgknLA2_5F2x-X4RyXqHWqgKc5cEhMJpV2lehCgsgMKm1mZHujFWWwBkN5phIzIrxS_WMp5Xx5-GF6evj3MR-Ra050_EFym2yt-6_4mFyFb-t66J_4E_Eb-UEXGg priority: 102 providerName: ProQuest
Title	Memristors—From In‐Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio‐Inspired Computing
URI	https://onlinelibrary.wiley.com/doi/abs/10.1002%2Faisy.202000085 https://www.proquest.com/docview/2822734111 https://doaj.org/article/ebb5ac157c1c4f68ab829098c26feabf
Volume	2
WOSCitedRecordID	wos000669793800002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2640-4567 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171027 issn: 2640-4567 databaseCode: DOA dateStart: 20190101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2640-4567 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171027 issn: 2640-4567 databaseCode: M~E dateStart: 20190101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2640-4567 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171027 issn: 2640-4567 databaseCode: P5Z dateStart: 20201001 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2640-4567 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171027 issn: 2640-4567 databaseCode: BENPR dateStart: 20201001 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2640-4567 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171027 issn: 2640-4567 databaseCode: PIMPY dateStart: 20201001 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 2640-4567 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171027 issn: 2640-4567 databaseCode: WIN dateStart: 20190101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 2640-4567 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002171027 issn: 2640-4567 databaseCode: 24P dateStart: 20190101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELag5cCFhwCxUFY-IHFp1Nh5Ocdd6IqV2CiiPFoukT07biPRZJUsSL31J3Dgxr_rL8F2smn3gJDgEsXW-CF77BmPx98Q8jKMlFSxFF6kOHghsNCTPFaeLxAxRIapw-n-9C7JMnF8nOY3XvF3-BCDwc2uDLdf2wUuVXtwDRoqy_bCnO9452t1m-wyFiSWr3mYD1YWo3AbCWrfTBvB73tGW0g2yI0-P9iuYksyOQD_La3zpu7qhM_s_v93-wG51yuedNJxykNyC6tH5NcCzx24QNNeXf6cNfU5nVdXlz8W1v_2gnYhH4xw26dvEFe0B2M9pRMAI6465tmnslrSo1Vpje7Ugn2YZrLOu7yl65oaFZPOHHQJrbUjqE3tq7MSXMlpWZsW55W98sfldZuPycfZ4YfXb70-XIMHoY34qFgagi-F1AxSzaRAHQeBRox0DJEGP9DmvCN8rlJhFDsVYIIB40umUwSMIXhCdqq6wqeEamFORb70mdZmkwlCFZuqdRJrDksuAUfE20xVAT2WuQ2p8bXoUJh5YYe5GIZ5RF4N9KsOxeOPlFM78wOVRd92GXVzWvSLuUClIgksSoBBqGMhlb2OTgXwWKNUekT2NnxT9FtCW1h_3cToDIyNCHcc8peuFJP50cmQevYvhZ6Tu_a_ezu5R3bWzTd8Qe7A93XZNmO3VMZkd3qY5e_Hzhphvnn0xeTl80V-YlKf59lvcH0f2A
linkProvider	Wiley-Blackwell
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LbxMxEB6VFAmExBsRKOADiEtXXXvfB4RSStRVkyhSC2pPW3syLpHobsgGUG78CCT-BT-KX4K9j7Q9AKceOO5qbK-8n-dhj78BeO4HSqpQxk6gBDo-ct-RIlSOGxORT5ySiqf7_SAajeLDw2S8Bj_buzA2rbLViZWinhRo98i3bLpjZFQu569nnxxbNcqerrYlNGpY7NHyqwnZylfpjvm_L4Tovz14s-s0VQUc9G1hQmXCdnRlLDXHRHMZkw49TxMFOsRAo-tp45bHrlBJbPwP5VFEHhcTrhNCCtEz_V6Bdd-CvQPr43Q4Plrt6hgH31jsqGWHdMWWnJZLE4aKOiXsgvWrigRc8GzP-8eVgevf-t-m5jbcbFxp1quxfwfWKL8LN3rnTkbuwY8hnVbsCfPy17fv_XlxytLcGdr04iWrK1oY273JdohmrOGaPWE9RGON67WxyWQ-YfuzqT1TYJbLxIw5qpPnS7YomPGgWb9iZmGFrgQK0_vswxSrltvTwklzm89Ak7MR78O7S5maB9DJi5weAtOxCflc6XKtjQb1fBWarnUUaoETIZG64LQYybAharf1Qj5mNcW0yCymshWmuvByJT-rKUr-KLltIbeSstTi1YtifpI1miojpQKJPIiQo6_DWCp71p7EKEJNUukubLQozBp9V2ZnEOyCqED8j0_Jeun-0erp0d_7fAbXdg-Gg2yQjvYew3XbrL4OugGdxfwzPYGr-GUxLedPm_XI4Piycf4bV2t2tg
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtQwFLVKQQgh8UYdKOAFiE2jiZ33AqEpw4hR29FIBVTYGPvmuozUJkMygGbHRyDxL3wOX4LtJNN2Aay6YJnIj8g5vg_f63MJeRxGSqpYpl6kOHghsNCTPFaenyJiiAwzx9P9djeZTNKDg2y6Rn52d2FsWmUnE52gzkuwZ-R9m-6YGJHLWF-3aRHT4ej5_JNnK0jZSGtXTqOByA4uvxr3rX42Hpp__YTz0cvXL155bYUBD0JbpFAZFx58mUrNINNMpqjjINCIkY4h0uAH2pjoqc9VlhpbRAWYYMB4znSGgDEEZtwL5GJifEybTjiN3q_Od4ypb3R30vFE-rwvZ_XSOKS8SQ47owdduYAzNu5pS9mputH1_3mRbpBrrYFNB82OuEnWsLhFrg5OxUtukx97eOw4Far617fvo6o8puPC27NJx0va1LkwGn2LDhHntGWgPaQDAKOjmx2zRWWR0_35zEYaqGU4MXNOmpT6mi5KauxqOnJ8LbTUrkFpRp9_nIHruT0rvXFhsxwwP5nxDnlzLktzl6wXZYEbhOrUOIK-9JnWRq4GoYrN0DqJNYecS8Ae8Tq8CGjp220VkSPREE9zYfElVvjqkaer9vOGuOSPLbct_FatLOG4e1FWh6KVXwKViiSwKAEGoY5TqWwEPkuBxxql0j2y2SFStFKwFidw7BHuAP2PTxGD8f671dO9v4_5iFw24Ba748nOfXLF9mruiG6S9UX1GR-QS_BlMaurh25jUvLhvEH-G2zSfhk
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Memristors%E2%80%94From+In%E2%80%90Memory+Computing%2C+Deep+Learning+Acceleration%2C+and+Spiking+Neural+Networks+to+the+Future+of+Neuromorphic+and+Bio%E2%80%90Inspired+Computing&rft.jtitle=Advanced+intelligent+systems&rft.au=Mehonic%2C+Adnan&rft.au=Sebastian%2C+Abu&rft.au=Rajendran%2C+Bipin&rft.au=Simeone%2C+Osvaldo&rft.date=2020-11-01&rft.issn=2640-4567&rft.eissn=2640-4567&rft.volume=2&rft.issue=11&rft.epage=n%2Fa&rft_id=info:doi/10.1002%2Faisy.202000085&rft.externalDBID=10.1002%252Faisy.202000085&rft.externalDocID=AISY202000085
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2640-4567&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2640-4567&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2640-4567&client=summon