{"id":"https://openalex.org/W4416252056","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227706","title":"A Deep Reservoir Computing Architecture for Dynamic Generative Modeling","display_name":"A Deep Reservoir Computing Architecture for Dynamic Generative Modeling","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416252056","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227706"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227706","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227706","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100756628","display_name":"Wei Zhang","orcid":"https://orcid.org/0000-0001-5556-3896"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wei Zhang","raw_affiliation_strings":["Washington University in St. Louis,Department of Electrical & Systems Engineering,St. Louis,MO,USA"],"affiliations":[{"raw_affiliation_string":"Washington University in St. Louis,Department of Electrical & Systems Engineering,St. Louis,MO,USA","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112859961","display_name":"Yuan-Hung Kuan","orcid":"https://orcid.org/0009-0002-3641-980X"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuan-Hung Kuan","raw_affiliation_strings":["Washington University in St. Louis,Department of Electrical & Systems Engineering,St. Louis,MO,USA"],"affiliations":[{"raw_affiliation_string":"Washington University in St. Louis,Department of Electrical & Systems Engineering,St. Louis,MO,USA","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018469978","display_name":"Su\u2010Hsin Chang","orcid":"https://orcid.org/0000-0001-5872-9556"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Su-Hsin Chang","raw_affiliation_strings":["Washington University School of Medicine,Division of Public Health Sciences,Department of Surgery,St. Louis,MO,USA"],"affiliations":[{"raw_affiliation_string":"Washington University School of Medicine,Division of Public Health Sciences,Department of Surgery,St. Louis,MO,USA","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079314465","display_name":"Jr-Shin Li","orcid":"https://orcid.org/0000-0001-6693-3979"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jr-Shin Li","raw_affiliation_strings":["Washington University in St. Louis,Department of Electrical & Systems Engineering,St. Louis,MO,USA"],"affiliations":[{"raw_affiliation_string":"Washington University in St. Louis,Department of Electrical & Systems Engineering,St. Louis,MO,USA","institution_ids":["https://openalex.org/I204465549"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100756628"],"corresponding_institution_ids":["https://openalex.org/I204465549"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18315243,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9641000032424927,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9641000032424927,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.010999999940395355,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.007699999958276749,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reservoir-computing","display_name":"Reservoir computing","score":0.7045000195503235},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6972000002861023},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5051000118255615},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49149999022483826},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48410001397132874},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.4505000114440918},{"id":"https://openalex.org/keywords/dynamical-systems-theory","display_name":"Dynamical systems theory","score":0.3968999981880188},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.3912000060081482},{"id":"https://openalex.org/keywords/generative-design","display_name":"Generative Design","score":0.3889999985694885}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.73580002784729},{"id":"https://openalex.org/C135796866","wikidata":"https://www.wikidata.org/wiki/Q7315328","display_name":"Reservoir computing","level":4,"score":0.7045000195503235},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6972000002861023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6428999900817871},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5051000118255615},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49149999022483826},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48410001397132874},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4505000114440918},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44679999351501465},{"id":"https://openalex.org/C79379906","wikidata":"https://www.wikidata.org/wiki/Q3174497","display_name":"Dynamical systems theory","level":2,"score":0.3968999981880188},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3912000060081482},{"id":"https://openalex.org/C184408114","wikidata":"https://www.wikidata.org/wiki/Q1502022","display_name":"Generative Design","level":3,"score":0.3889999985694885},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.36239999532699585},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.3580000102519989},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3517000079154968},{"id":"https://openalex.org/C197298091","wikidata":"https://www.wikidata.org/wiki/Q5318963","display_name":"Dynamic data","level":2,"score":0.3513000011444092},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C37404715","wikidata":"https://www.wikidata.org/wiki/Q380679","display_name":"Dynamic programming","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.260699987411499},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227706","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227706","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309700","display_name":"Foundation for Barnes-Jewish Hospital","ror":"https://ror.org/04wyvkr12"},{"id":"https://openalex.org/F4320338440","display_name":"HORIZON EUROPE Health","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W239382782","https://openalex.org/W1971129545","https://openalex.org/W1989312953","https://openalex.org/W2021890002","https://openalex.org/W2022248835","https://openalex.org/W2036451492","https://openalex.org/W2113132555","https://openalex.org/W2115129628","https://openalex.org/W2150153237","https://openalex.org/W2158479357","https://openalex.org/W2162800060","https://openalex.org/W2169125069","https://openalex.org/W2171865010","https://openalex.org/W2531479711","https://openalex.org/W2587524409","https://openalex.org/W2608997467","https://openalex.org/W2611445505","https://openalex.org/W2625843652","https://openalex.org/W2735778951","https://openalex.org/W2774513877","https://openalex.org/W2887258823","https://openalex.org/W2889351375","https://openalex.org/W2896714202","https://openalex.org/W2922494440","https://openalex.org/W2942202612","https://openalex.org/W2964010366","https://openalex.org/W2971405544","https://openalex.org/W2979589704","https://openalex.org/W3012621877","https://openalex.org/W3022434801","https://openalex.org/W3038427506","https://openalex.org/W3047286661","https://openalex.org/W3081790702","https://openalex.org/W3087736720","https://openalex.org/W3106907685","https://openalex.org/W3122214732","https://openalex.org/W3128485317","https://openalex.org/W3132491925","https://openalex.org/W3136756438","https://openalex.org/W3214007446","https://openalex.org/W4200380717","https://openalex.org/W4200567002","https://openalex.org/W4206582666","https://openalex.org/W4221073399","https://openalex.org/W4293176126","https://openalex.org/W4379615800","https://openalex.org/W4379929627"],"related_works":[],"abstract_inverted_index":{"Reservoir":[0],"computing":[1,84],"networks":[2],"(RCNs)":[3],"have":[4],"been":[5],"recognized":[6],"as":[7,54,100,109,143],"a":[8,80,101,110,118,144],"popular":[9],"machine":[10],"learning":[11,130,170],"tool":[12],"for":[13,122,132],"modeling":[14,133],"the":[15,73,123,141,152,161],"temporal":[16],"evolution":[17],"of":[18,32,76,103,160],"dynamic":[19,163],"data":[20,150],"due":[21],"to":[22,63,139,147],"their":[23,35],"close":[24],"relationship":[25],"with":[26],"dynamical":[27,134,154],"systems.":[28,135,155],"A":[29],"defining":[30],"characteristic":[31],"RCNs":[33,105],"is":[34,98,107],"fixed":[36],"(training":[37],"parameter-free)":[38],"hidden":[39],"layers,":[40],"which":[41],"offers":[42],"significant":[43],"computational":[44],"benefits.":[45],"However,":[46],"this":[47],"feature":[48],"also":[49],"introduces":[50],"adverse":[51],"impacts,":[52],"such":[53],"increased":[55],"warm-up":[56],"time,":[57],"limited":[58],"long-term":[59],"memory,":[60],"and":[61,69,71,92,106,158,176,182],"sensitivity":[62],"hyperparameters.":[64],"To":[65],"balance":[66],"these":[67],"advantages":[68],"disadvantages":[70],"expand":[72],"application":[74],"domains":[75],"RCNs,":[77],"we":[78],"develop":[79],"novel":[81],"deep":[82,129],"reservoir":[83],"network":[85],"(DRCN)":[86],"architecture":[87,97],"that":[88],"integrates":[89],"control-theoretic":[90],"concepts":[91],"techniques":[93],"into":[94],"RCNs.":[95],"This":[96,136],"designed":[99],"cascade":[102],"shallow":[104],"represented":[108],"piecewise":[111],"time-invariant":[112],"control":[113,177],"system.":[114],"We":[115],"further":[116],"propose":[117],"layer-by-layer":[119],"training":[120],"strategy":[121],"DRCN,":[124],"resulting":[125],"in":[126],"an":[127],"iterative":[128],"algorithm":[131],"enables":[137],"us":[138],"exploit":[140],"DRCN":[142],"generative":[145,164],"model":[146,165],"generate":[148],"output-of-sample":[149],"using":[151,179],"learned":[153],"The":[156],"performance":[157],"efficiency":[159],"DRCN-based":[162],"are":[166],"demonstrated":[167],"through":[168],"various":[169],"problems":[171],"arising":[172],"from":[173],"time-series":[174],"analysis":[175],"systems,":[178],"both":[180],"synthetic":[181],"real-world":[183],"datasets.":[184]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}