{"id":"https://openalex.org/W2735207540","doi":"https://doi.org/10.1109/ijcnn.2017.7966336","title":"A synaptic plasticity rule providing a unified approach to supervised and unsupervised learning","display_name":"A synaptic plasticity rule providing a unified approach to supervised and unsupervised learning","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2735207540","doi":"https://doi.org/10.1109/ijcnn.2017.7966336","mag":"2735207540"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7966336","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966336","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 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/A5031421685","display_name":"Mikhail Kiselev","orcid":"https://orcid.org/0000-0001-7403-6418"},"institutions":[{"id":"https://openalex.org/I181171540","display_name":"Chuvash State University","ror":"https://ror.org/01jmd7f74","country_code":"RU","type":"education","lineage":["https://openalex.org/I181171540"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Mikhail Kiselev","raw_affiliation_strings":["Department of Mathematics and Information Technologies, Chuvash State University, Cheboxary, Russia"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Information Technologies, Chuvash State University, Cheboxary, Russia","institution_ids":["https://openalex.org/I181171540"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5031421685"],"corresponding_institution_ids":["https://openalex.org/I181171540"],"apc_list":null,"apc_paid":null,"fwci":0.2922,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.59402737,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3806","last_page":"3813"},"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":0.9983999729156494,"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/T11601","display_name":"Neuroscience and Neural Engineering","score":0.9972000122070312,"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/hebbian-theory","display_name":"Hebbian theory","score":0.8160245418548584},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.7902748584747314},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7050987482070923},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6724584102630615},{"id":"https://openalex.org/keywords/synaptic-weight","display_name":"Synaptic weight","score":0.6683008074760437},{"id":"https://openalex.org/keywords/learning-rule","display_name":"Learning rule","score":0.6499629616737366},{"id":"https://openalex.org/keywords/competitive-learning","display_name":"Competitive learning","score":0.6490066051483154},{"id":"https://openalex.org/keywords/leabra","display_name":"Leabra","score":0.5295349359512329},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5278551578521729},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5136906504631042},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5050526261329651},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5023953914642334},{"id":"https://openalex.org/keywords/synaptic-plasticity","display_name":"Synaptic plasticity","score":0.4590860903263092},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.43761131167411804},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3680278956890106},{"id":"https://openalex.org/keywords/wake-sleep-algorithm","display_name":"Wake-sleep algorithm","score":0.2645852565765381},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.07999244332313538}],"concepts":[{"id":"https://openalex.org/C111437709","wikidata":"https://www.wikidata.org/wiki/Q1277874","display_name":"Hebbian theory","level":3,"score":0.8160245418548584},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.7902748584747314},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7050987482070923},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6724584102630615},{"id":"https://openalex.org/C66949984","wikidata":"https://www.wikidata.org/wiki/Q7662043","display_name":"Synaptic weight","level":3,"score":0.6683008074760437},{"id":"https://openalex.org/C2779127903","wikidata":"https://www.wikidata.org/wiki/Q6510194","display_name":"Learning rule","level":3,"score":0.6499629616737366},{"id":"https://openalex.org/C120822770","wikidata":"https://www.wikidata.org/wiki/Q5156355","display_name":"Competitive learning","level":3,"score":0.6490066051483154},{"id":"https://openalex.org/C97108695","wikidata":"https://www.wikidata.org/wiki/Q6508265","display_name":"Leabra","level":5,"score":0.5295349359512329},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5278551578521729},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5136906504631042},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5050526261329651},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5023953914642334},{"id":"https://openalex.org/C98229152","wikidata":"https://www.wikidata.org/wiki/Q1551556","display_name":"Synaptic plasticity","level":3,"score":0.4590860903263092},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.43761131167411804},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3680278956890106},{"id":"https://openalex.org/C17061570","wikidata":"https://www.wikidata.org/wiki/Q7960888","display_name":"Wake-sleep algorithm","level":4,"score":0.2645852565765381},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.07999244332313538},{"id":"https://openalex.org/C117765406","wikidata":"https://www.wikidata.org/wiki/Q5362437","display_name":"Generalization error","level":3,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C170493617","wikidata":"https://www.wikidata.org/wiki/Q208467","display_name":"Receptor","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2017.7966336","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966336","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W319463805","https://openalex.org/W1570411240","https://openalex.org/W1627996140","https://openalex.org/W1733248925","https://openalex.org/W1970109917","https://openalex.org/W1990221069","https://openalex.org/W1994984174","https://openalex.org/W2001619934","https://openalex.org/W2053163863","https://openalex.org/W2054113233","https://openalex.org/W2057737850","https://openalex.org/W2102916183","https://openalex.org/W2110654393","https://openalex.org/W2115831804","https://openalex.org/W2138128929","https://openalex.org/W2147101007","https://openalex.org/W2371946849","https://openalex.org/W2398014893","https://openalex.org/W2482313744","https://openalex.org/W2549083315","https://openalex.org/W2569813014","https://openalex.org/W4323913440","https://openalex.org/W6708635637","https://openalex.org/W6712680518"],"related_works":["https://openalex.org/W2159992686","https://openalex.org/W2026544443","https://openalex.org/W1530541224","https://openalex.org/W1978872345","https://openalex.org/W2183231132","https://openalex.org/W2943484315","https://openalex.org/W1597526543","https://openalex.org/W2158831520","https://openalex.org/W2466392136","https://openalex.org/W1520894008"],"abstract_inverted_index":{"At":[0],"the":[1,31,35,89,109],"early":[2],"stages":[3],"of":[4,16,30,52,62,92,112],"their":[5],"exploration,":[6],"spiking":[7],"neural":[8,37],"networks":[9,38],"were":[10],"considered":[11,124],"mainly":[12],"as":[13,34,165],"plausible":[14],"models":[15],"biological":[17,90],"neuronal":[18],"ensembles,":[19],"but":[20],"then":[21],"it":[22,98,137],"became":[23],"clear":[24],"that":[25,160],"they":[26],"can":[27,163],"solve":[28],"many":[29],"same":[32],"problems":[33],"traditional":[36],"-":[39,69,84,144],"classification":[40],"and":[41,46,77,152,171,177],"clustering":[42],"using":[43],"various":[44],"supervised":[45,86,172],"unsupervised":[47,75,170],"learning":[48,63,71,76,173],"methods.":[49],"The":[50,114],"majority":[51],"synaptic":[53,116,155],"weight":[54],"adjustment":[55],"algorithms":[56,95,174],"proposed":[57],"for":[58,74,85,108,141,146,154,168],"these":[59],"two":[60],"kinds":[61,111],"are":[64],"based":[65],"on":[66],"different":[67,120],"principles":[68],"Hebbian":[70,106],"(STDP":[72],"etc.)":[73],"gradient":[78,93],"methods":[79],"similar":[80],"to":[81,102,149],"error":[82],"backpropagation":[83],"learning.":[87,113],"Since":[88],"plausibility":[91],"descent":[94],"is":[96,123,129,158],"doubtful,":[97],"would":[99],"be":[100],"desirable":[101],"find":[103],"a":[104,166],"purely":[105],"mechanism":[107],"both":[110],"appropriate":[115],"plasticity":[117],"law":[118],"(though":[119],"from":[121],"STDP)":[122],"in":[125],"this":[126,161],"paper.":[127],"It":[128,157],"called":[130,175],"2TP":[131],"(Two":[132],"Threshold":[133],"Plasticity)":[134],"rule":[135,162],"because":[136],"includes":[138],"2":[139],"thresholds":[140],"membrane":[142],"potential":[143],"one":[145,153],"firing,":[147],"leading":[148],"synapse":[150],"potentiation,":[151],"depression.":[156],"demonstrated":[159],"serve":[164],"basis":[167],"efficient":[169],"L2TP/U":[176],"L2TP/S,":[178],"respectively.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-19T08:26:33.389920","created_date":"2025-10-10T00:00:00"}