{"id":"https://openalex.org/W4377047463","doi":"https://doi.org/10.1088/2634-4386/acd6b3","title":"Conduction and entropy analysis of a mixed memristor-resistor model for neuromorphic networks","display_name":"Conduction and entropy analysis of a mixed memristor-resistor model for neuromorphic networks","publication_year":2023,"publication_date":"2023-05-18","ids":{"openalex":"https://openalex.org/W4377047463","doi":"https://doi.org/10.1088/2634-4386/acd6b3"},"language":"en","primary_location":{"id":"doi:10.1088/2634-4386/acd6b3","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2634-4386/acd6b3","pdf_url":"https://iopscience.iop.org/article/10.1088/2634-4386/acd6b3/pdf","source":{"id":"https://openalex.org/S4210212933","display_name":"Neuromorphic Computing and Engineering","issn_l":"2634-4386","issn":["2634-4386"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neuromorphic Computing and Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://iopscience.iop.org/article/10.1088/2634-4386/acd6b3/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019243704","display_name":"Davide Cipollini","orcid":"https://orcid.org/0000-0003-4350-8691"},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Davide Cipollini","raw_affiliation_strings":["Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Nijenborgh 9, GRONINGEN, 9747 AG, NETHERLANDS"],"raw_orcid":"https://orcid.org/0000-0003-4350-8691","affiliations":[{"raw_affiliation_string":"Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Nijenborgh 9, GRONINGEN, 9747 AG, NETHERLANDS","institution_ids":["https://openalex.org/I169381384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028858025","display_name":"Lambert Schomaker","orcid":"https://orcid.org/0000-0003-2351-930X"},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Lambert R B Schomaker","raw_affiliation_strings":["Artificial Intelligence, University of Groningen, Nijenborgh 9, Groningen, 9747 AG, NETHERLANDS"],"raw_orcid":"https://orcid.org/0000-0003-2351-930X","affiliations":[{"raw_affiliation_string":"Artificial Intelligence, University of Groningen, Nijenborgh 9, Groningen, 9747 AG, NETHERLANDS","institution_ids":["https://openalex.org/I169381384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5019243704"],"corresponding_institution_ids":["https://openalex.org/I169381384"],"apc_list":{"value":2000,"currency":"GBP","value_usd":2453},"apc_paid":{"value":2000,"currency":"GBP","value_usd":2453},"fwci":0.5064,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.63043973,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"3","issue":"3","first_page":"034001","last_page":"034001"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12236","display_name":"Photoreceptor and optogenetics research","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.8995866179466248},{"id":"https://openalex.org/keywords/memristor","display_name":"Memristor","score":0.8616613745689392},{"id":"https://openalex.org/keywords/resistor","display_name":"Resistor","score":0.608673095703125},{"id":"https://openalex.org/keywords/conductance","display_name":"Conductance","score":0.5411732196807861},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.5028957724571228},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47224539518356323},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4105231761932373},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3888983726501465},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.38589298725128174},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.36534368991851807},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.3645161986351013},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.35027769207954407},{"id":"https://openalex.org/keywords/condensed-matter-physics","display_name":"Condensed matter physics","score":0.314441442489624},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.21879112720489502},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21122512221336365},{"id":"https://openalex.org/keywords/thermodynamics","display_name":"Thermodynamics","score":0.1255531907081604},{"id":"https://openalex.org/keywords/quantum-mechanics","display_name":"Quantum mechanics","score":0.12490257620811462},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11857622861862183}],"concepts":[{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.8995866179466248},{"id":"https://openalex.org/C150072547","wikidata":"https://www.wikidata.org/wiki/Q212923","display_name":"Memristor","level":2,"score":0.8616613745689392},{"id":"https://openalex.org/C137488568","wikidata":"https://www.wikidata.org/wiki/Q5321","display_name":"Resistor","level":3,"score":0.608673095703125},{"id":"https://openalex.org/C121932024","wikidata":"https://www.wikidata.org/wiki/Q5159376","display_name":"Conductance","level":2,"score":0.5411732196807861},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.5028957724571228},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47224539518356323},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4105231761932373},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3888983726501465},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.38589298725128174},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.36534368991851807},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.3645161986351013},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.35027769207954407},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.314441442489624},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.21879112720489502},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21122512221336365},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.1255531907081604},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.12490257620811462},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11857622861862183}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1088/2634-4386/acd6b3","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2634-4386/acd6b3","pdf_url":"https://iopscience.iop.org/article/10.1088/2634-4386/acd6b3/pdf","source":{"id":"https://openalex.org/S4210212933","display_name":"Neuromorphic Computing and Engineering","issn_l":"2634-4386","issn":["2634-4386"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neuromorphic Computing and Engineering","raw_type":"journal-article"},{"id":"pmh:oai:pure.rug.nl:openaire_cris_publications/3f44f1db-73f6-4591-bbe3-4b58dff6bc5e","is_oa":true,"landing_page_url":"https://research.rug.nl/en/publications/3f44f1db-73f6-4591-bbe3-4b58dff6bc5e","pdf_url":null,"source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cipollini, D & Schomaker, L 2023, 'Conduction and entropy analysis of a mixed memristor-resistor model for neuromorphic networks', Neuromorphic computing and engineering, vol. 3, no. 3, acd6b3. https://doi.org/10.1088/2634-4386/acd6b3","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:doaj.org/article:ec358bb4434949f2966690c1e426fc18","is_oa":true,"landing_page_url":"https://doaj.org/article/ec358bb4434949f2966690c1e426fc18","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Neuromorphic Computing and Engineering, Vol 3, Iss 3, p 034001 (2023)","raw_type":"article"},{"id":"pmh:oai:pure.rug.nl:openaire/3f44f1db-73f6-4591-bbe3-4b58dff6bc5e","is_oa":true,"landing_page_url":"https://hdl.handle.net/11370/3f44f1db-73f6-4591-bbe3-4b58dff6bc5e","pdf_url":null,"source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Cipollini, D & Schomaker, L 2023, 'Conduction and entropy analysis of a mixed memristor-resistor model for neuromorphic networks', Neuromorphic computing and engineering, vol. 3, no. 3, acd6b3. https://doi.org/10.1088/2634-4386/acd6b3","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1088/2634-4386/acd6b3","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2634-4386/acd6b3","pdf_url":"https://iopscience.iop.org/article/10.1088/2634-4386/acd6b3/pdf","source":{"id":"https://openalex.org/S4210212933","display_name":"Neuromorphic Computing and Engineering","issn_l":"2634-4386","issn":["2634-4386"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neuromorphic Computing and Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6800000071525574,"display_name":"No poverty","id":"https://metadata.un.org/sdg/1"}],"awards":[{"id":"https://openalex.org/G13992887","display_name":"Materials for Neuromorphic Circuits","funder_award_id":"861153","funder_id":"https://openalex.org/F4320338337","funder_display_name":"H2020 Marie Sk\u0142odowska-Curie Actions"}],"funders":[{"id":"https://openalex.org/F4320338337","display_name":"H2020 Marie Sk\u0142odowska-Curie Actions","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4377047463.pdf"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1985425094","https://openalex.org/W2006119291","https://openalex.org/W2020971886","https://openalex.org/W2058588292","https://openalex.org/W2094996340","https://openalex.org/W2101091847","https://openalex.org/W2139030761","https://openalex.org/W2508745127","https://openalex.org/W2513813309","https://openalex.org/W2526646482","https://openalex.org/W2548566911","https://openalex.org/W2767939831","https://openalex.org/W2796178394","https://openalex.org/W2804023226","https://openalex.org/W2884285099","https://openalex.org/W2891145414","https://openalex.org/W2923010225","https://openalex.org/W2964039969","https://openalex.org/W3009734414","https://openalex.org/W3042277388","https://openalex.org/W3043940359","https://openalex.org/W3098399805","https://openalex.org/W3106383679","https://openalex.org/W3122523307","https://openalex.org/W3145910713","https://openalex.org/W3190634970","https://openalex.org/W3204457379","https://openalex.org/W4205182105","https://openalex.org/W4206609022","https://openalex.org/W4210912906","https://openalex.org/W4220711214","https://openalex.org/W4287023910","https://openalex.org/W4308862048","https://openalex.org/W4313893168","https://openalex.org/W6729502873","https://openalex.org/W6800404361"],"related_works":["https://openalex.org/W1872623660","https://openalex.org/W4292697011","https://openalex.org/W3207218810","https://openalex.org/W3212508523","https://openalex.org/W1995352804","https://openalex.org/W2086672837","https://openalex.org/W2016922062","https://openalex.org/W4367187682","https://openalex.org/W2909534142","https://openalex.org/W3215957123"],"abstract_inverted_index":{"Abstract":[0],"To":[1],"build":[2],"neuromorphic":[3,59],"hardware":[4],"with":[5],"self-assembled":[6,58],"memristive":[7,93,124,196,216],"networks,":[8],"it":[9],"is":[10,50,175,185],"necessary":[11],"to":[12,65,160,187,249],"determine":[13],"how":[14],"the":[15,24,42,54,73,78,83,86,92,96,101,110,114,120,127,137,145,148,152,161,167,171,189,200,207,212,215,225,229,233,250,263,266,273],"functional":[16],"connectivity":[17],"between":[18,164],"electrodes":[19,165],"can":[20],"be":[21,66],"adjusted,":[22],"under":[23,232],"application":[25,234],"of":[26,36,44,56,80,85,91,98,103,105,119,139,147,154,170,194,202,214,224,235,256,265],"external":[27],"signals.":[28],"In":[29],"this":[30],"work,":[31],"we":[32],"analyse":[33],"a":[34,37,48,117,203,219,222,236],"model":[35,49],"disordered":[38],"memristor-resistor":[39],"network,":[40],"within":[41],"framework":[43],"graph":[45],"theory.":[46],"Such":[47],"well":[51],"suited":[52],"for":[53,253],"simulation":[55],"physical":[57],"materials":[60],"where":[61,116],"impurities":[62],"are":[63,76,156],"likely":[64],"present.":[67],"Two":[68],"primary":[69],"mechanisms":[70],"that":[71],"modulate":[72],"collective":[74],"dynamics":[75],"investigated:":[77],"strength":[79,150,210],"interaction,":[81],"i.e.":[82],"ratio":[84],"two":[87],"limiting":[88],"conductance":[89,231],"states":[90],"components,":[94],"and":[95,141,151,191,211],"role":[97,146],"disorder":[99],"in":[100,158,199,218,228,245,262,275],"form":[102],"density":[104],"Ohmic":[106,132],"conductors":[107],"(OCs)":[108],"diluting":[109],"network.":[111],"We":[112,134],"consider":[113,135],"case":[115,138],"fraction":[118,217],"network":[121,183,220,230],"edges":[122],"has":[123],"properties,":[125],"while":[126],"remaining":[128],"part":[129],"shows":[130,242],"pure":[131],"behaviour.":[133],"both":[136],"poor":[140,260],"good":[142],"OCs.":[143],"Both":[144,206],"interaction":[149,209],"presence":[153],"OCs":[155,261],"investigated":[157],"relation":[159],"trace":[162,247,276],"formation":[163,201,248],"at":[166],"fixed":[168],"point":[169],"dynamics.":[172],"The":[173,269],"latter":[174],"analysed":[176],"through":[177],"an":[178],"ideal":[179],"observer":[180],"approach.":[181],"Thus,":[182],"entropy":[184],"used":[186],"understand":[188],"self-reinforcing":[190],"cooperative":[192],"inhibition":[193],"other":[195],"elements":[197],"resulting":[198],"winner-take-all":[204],"path.":[205],"low":[208],"dilution":[213],"provide":[221],"reduction":[223],"steep":[226],"non-linearity":[227],"steady":[237],"input":[238,270],"voltage.":[239],"Entropy":[240],"analysis":[241],"enhanced":[243],"robustness":[244],"selective":[246],"applied":[251],"voltage":[252,271],"heterogeneous":[254],"networks":[255],"memristors":[257],"diluted":[258],"by":[259],"vicinity":[264],"percolation":[267],"threshold.":[268],"controls":[272],"diversity":[274],"formation.":[277]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}