{"id":"https://openalex.org/W3011428992","doi":"https://doi.org/10.1109/access.2020.2981346","title":"A Supervised Learning Algorithm for Spiking Neurons Using Spike Train Kernel Based on a Unit of Pair-Spike","display_name":"A Supervised Learning Algorithm for Spiking Neurons Using Spike Train Kernel Based on a Unit of Pair-Spike","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3011428992","doi":"https://doi.org/10.1109/access.2020.2981346","mag":"3011428992"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2981346","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2981346","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09039652.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09039652.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100623856","display_name":"Guojun Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guojun Chen","raw_affiliation_strings":["School of Urban Design, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-1340-160X","affiliations":[{"raw_affiliation_string":"School of Urban Design, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045686512","display_name":"Guoen Wang","orcid":"https://orcid.org/0000-0002-8315-7913"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoen Wang","raw_affiliation_strings":["School of Urban Design, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-8315-7913","affiliations":[{"raw_affiliation_string":"School of Urban Design, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100623856"],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.4161,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.60316037,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"8","issue":null,"first_page":"53427","last_page":"53442"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9997000098228455,"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/T10320","display_name":"Neural Networks and Applications","score":0.9987000226974487,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spike","display_name":"Spike (software development)","score":0.9525175094604492},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.700011134147644},{"id":"https://openalex.org/keywords/spike-train","display_name":"Spike train","score":0.587439775466919},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5706257820129395},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5485117435455322},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5009639263153076},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4855673015117645},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.47889748215675354},{"id":"https://openalex.org/keywords/unit","display_name":"Unit (ring theory)","score":0.4585977792739868},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.326722115278244},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.24791505932807922},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20941299200057983},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.08231642842292786}],"concepts":[{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.9525175094604492},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.700011134147644},{"id":"https://openalex.org/C2909946758","wikidata":"https://www.wikidata.org/wiki/Q194277","display_name":"Spike train","level":3,"score":0.587439775466919},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5706257820129395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5485117435455322},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5009639263153076},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4855673015117645},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.47889748215675354},{"id":"https://openalex.org/C122637931","wikidata":"https://www.wikidata.org/wiki/Q118084","display_name":"Unit (ring theory)","level":2,"score":0.4585977792739868},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.326722115278244},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.24791505932807922},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20941299200057983},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.08231642842292786},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0},{"id":"https://openalex.org/C145420912","wikidata":"https://www.wikidata.org/wiki/Q853077","display_name":"Mathematics education","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.2981346","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2981346","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09039652.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8dcfc4d82b4a422097e406e5c89ca618","is_oa":true,"landing_page_url":"https://doaj.org/article/8dcfc4d82b4a422097e406e5c89ca618","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":"IEEE Access, Vol 8, Pp 53427-53442 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2981346","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2981346","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09039652.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2010370547","display_name":null,"funder_award_id":"51878516","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3011428992.pdf","grobid_xml":"https://content.openalex.org/works/W3011428992.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W267428601","https://openalex.org/W1486852018","https://openalex.org/W1559381497","https://openalex.org/W1561240200","https://openalex.org/W1937233719","https://openalex.org/W1965678517","https://openalex.org/W1974983944","https://openalex.org/W1985383357","https://openalex.org/W1991693778","https://openalex.org/W2001465022","https://openalex.org/W2016639237","https://openalex.org/W2019304693","https://openalex.org/W2023345584","https://openalex.org/W2055164551","https://openalex.org/W2081817157","https://openalex.org/W2107433900","https://openalex.org/W2108995755","https://openalex.org/W2113420865","https://openalex.org/W2126699012","https://openalex.org/W2130974072","https://openalex.org/W2141764232","https://openalex.org/W2146871603","https://openalex.org/W2152280100","https://openalex.org/W2154616847","https://openalex.org/W2165639766","https://openalex.org/W2170968634","https://openalex.org/W2171111726","https://openalex.org/W2239861374","https://openalex.org/W2290982066","https://openalex.org/W2373982822","https://openalex.org/W2490587419","https://openalex.org/W2512805308","https://openalex.org/W2529327711","https://openalex.org/W2560356199","https://openalex.org/W2569813014","https://openalex.org/W2587289508","https://openalex.org/W2605353576","https://openalex.org/W2789475128","https://openalex.org/W2798878556","https://openalex.org/W2805852360","https://openalex.org/W2919115771","https://openalex.org/W2919630283","https://openalex.org/W2924112519","https://openalex.org/W2945308847","https://openalex.org/W2949305633","https://openalex.org/W2963074136","https://openalex.org/W2963967818","https://openalex.org/W2969530516","https://openalex.org/W2972234767","https://openalex.org/W2974328520","https://openalex.org/W2979879900","https://openalex.org/W6633394554","https://openalex.org/W6682505573","https://openalex.org/W6762787388"],"related_works":["https://openalex.org/W2383615955","https://openalex.org/W4306175885","https://openalex.org/W2056756835","https://openalex.org/W4312604567","https://openalex.org/W4297619707","https://openalex.org/W4387390134","https://openalex.org/W4251092571","https://openalex.org/W2187212244","https://openalex.org/W2542565870","https://openalex.org/W2583316550"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"neuroscientists":[3],"have":[4,38,48],"discovered":[5],"that":[6,218],"the":[7,23,39,56,82,98,116,128,145,156,181,193,223],"neural":[8],"information":[9],"is":[10,170,190,207],"encoded":[11],"by":[12,131,149],"spike":[13,86,101,136,161,178],"trains":[14,87],"with":[15,211],"precise":[16,24],"times.":[17],"Supervised":[18],"learning":[19,41,76,175,196,200,202,205,225,229],"algorithm":[20,77,169,221],"based":[21,88,114],"on":[22,89,115],"times":[25],"for":[26,78],"spiking":[27,59,79],"neurons":[28,80],"becomes":[29],"an":[30,68,106,150],"important":[31],"research":[32],"field.":[33],"Although":[34],"many":[35,174],"existing":[36],"algorithms":[37],"excellent":[40],"ability,":[42],"most":[43],"of":[44,58,85,92,100,112,118,159,177,183,195],"their":[45],"mechanisms":[46],"still":[47],"some":[49,123],"complex":[50],"computations":[51,125],"and":[52,109,126,163,187,192,204,227],"certain":[53],"limitations.":[54],"Moreover,":[55,209],"discontinuity":[57],"process":[60,162],"also":[61],"makes":[62],"it":[63],"very":[64],"difficult":[65],"to":[66,154,173],"build":[67],"efficient":[69],"algorithm.":[70],"This":[71,120],"paper":[72],"proposes":[73],"a":[74,90],"supervised":[75],"using":[81,132],"kernel":[83,146],"function":[84,147],"unit":[91,117],"pair-spike.":[93,119],"Firstly,":[94],"we":[95,104,143],"comprehensively":[96],"divide":[97],"intervals":[99],"trains.":[102],"Then,":[103],"construct":[105],"optimal":[107,185],"selection":[108,186],"computation":[110,188],"method":[111,121,189],"spikes":[113],"avoids":[122],"wrong":[124],"reduces":[127],"computational":[129],"cost":[130],"each":[133],"effective":[134],"input":[135],"only":[137],"once":[138],"in":[139],"every":[140],"epoch.":[141],"Finally,":[142],"use":[144],"defined":[148],"inner":[151],"product":[152],"operator":[153],"solve":[155],"computing":[157],"problem":[158],"discontinue":[160],"multiple":[164],"output":[165],"spikes.":[166],"The":[167],"proposed":[168,220],"successfully":[171],"applied":[172],"tasks":[176],"trains,":[179],"where":[180],"effect":[182],"our":[184,219],"verified":[191],"influence":[194],"factors":[197],"such":[198],"as":[199],"kernel,":[201],"rate,":[203],"epoch":[206],"analyzed.":[208],"compared":[210],"other":[212],"algorithms,":[213],"all":[214],"experimental":[215],"results":[216],"show":[217],"has":[222],"higher":[224],"accuracy":[226],"good":[228],"efficiency.":[230]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}