{"id":"https://openalex.org/W2972809290","doi":"https://doi.org/10.1145/3354265.3354275","title":"Approximating Back-propagation for a Biologically Plausible Local Learning Rule in Spiking Neural Networks","display_name":"Approximating Back-propagation for a Biologically Plausible Local Learning Rule in Spiking Neural Networks","publication_year":2019,"publication_date":"2019-07-23","ids":{"openalex":"https://openalex.org/W2972809290","doi":"https://doi.org/10.1145/3354265.3354275","mag":"2972809290"},"language":"en","primary_location":{"id":"doi:10.1145/3354265.3354275","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3354265.3354275","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3354265.3354275","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Neuromorphic Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3354265.3354275","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053617387","display_name":"Amar Shrestha","orcid":"https://orcid.org/0000-0002-5988-0466"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Amar Shrestha","raw_affiliation_strings":["Syracuse University, Syracuse, NY, USA"],"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112473990","display_name":"Haowen Fang","orcid":"https://orcid.org/0009-0009-7551-3373"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haowen Fang","raw_affiliation_strings":["Syracuse University, Syracuse, NY, USA"],"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101676852","display_name":"Qing Wu","orcid":"https://orcid.org/0000-0001-7248-9755"},"institutions":[{"id":"https://openalex.org/I1280414376","display_name":"United States Air Force Research Laboratory","ror":"https://ror.org/02e2egq70","country_code":"US","type":"facility","lineage":["https://openalex.org/I1280414376","https://openalex.org/I1330347796","https://openalex.org/I4210102105","https://openalex.org/I4389425425"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qing Wu","raw_affiliation_strings":["US Air Force Research, Laboratory, NY, USA"],"affiliations":[{"raw_affiliation_string":"US Air Force Research, Laboratory, NY, USA","institution_ids":["https://openalex.org/I1280414376"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018468480","display_name":"Qinru Qiu","orcid":"https://orcid.org/0000-0003-2546-0655"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qinru Qiu","raw_affiliation_strings":["Syracuse University, Syracuse, NY, USA"],"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5053617387"],"corresponding_institution_ids":["https://openalex.org/I70983195"],"apc_list":null,"apc_paid":null,"fwci":2.9051,"has_fulltext":true,"cited_by_count":39,"citation_normalized_percentile":{"value":0.9141127,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"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/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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9980000257492065,"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":0.9954000115394592,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.8176800012588501},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.7824278473854065},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7575192451477051},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.7245740294456482},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5853990912437439},{"id":"https://openalex.org/keywords/learning-rule","display_name":"Learning rule","score":0.5419240593910217},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4854922592639923},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.45427191257476807},{"id":"https://openalex.org/keywords/spike","display_name":"Spike (software development)","score":0.45234620571136475},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.433120995759964},{"id":"https://openalex.org/keywords/rprop","display_name":"Rprop","score":0.42555907368659973},{"id":"https://openalex.org/keywords/synaptic-weight","display_name":"Synaptic weight","score":0.4159574806690216},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35476768016815186},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.28774476051330566},{"id":"https://openalex.org/keywords/types-of-artificial-neural-networks","display_name":"Types of artificial neural networks","score":0.24498531222343445}],"concepts":[{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.8176800012588501},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.7824278473854065},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7575192451477051},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.7245740294456482},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5853990912437439},{"id":"https://openalex.org/C2779127903","wikidata":"https://www.wikidata.org/wiki/Q6510194","display_name":"Learning rule","level":3,"score":0.5419240593910217},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4854922592639923},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.45427191257476807},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.45234620571136475},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.433120995759964},{"id":"https://openalex.org/C98359873","wikidata":"https://www.wikidata.org/wiki/Q1320470","display_name":"Rprop","level":5,"score":0.42555907368659973},{"id":"https://openalex.org/C66949984","wikidata":"https://www.wikidata.org/wiki/Q7662043","display_name":"Synaptic weight","level":3,"score":0.4159574806690216},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35476768016815186},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.28774476051330566},{"id":"https://openalex.org/C177973122","wikidata":"https://www.wikidata.org/wiki/Q7860946","display_name":"Types of artificial neural networks","level":4,"score":0.24498531222343445},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3354265.3354275","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3354265.3354275","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3354265.3354275","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Neuromorphic Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3354265.3354275","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3354265.3354275","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3354265.3354275","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Neuromorphic Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8999999761581421}],"awards":[{"id":"https://openalex.org/G3492751910","display_name":null,"funder_award_id":"CNS-1650469","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3790410446","display_name":"Syracuse University Planning Grant: I/UCRC for Alternative Sustainable and Intelligent Computing","funder_award_id":"1650469","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338294","display_name":"Air Force Research Laboratory","ror":"https://ror.org/02e2egq70"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2972809290.pdf","grobid_xml":"https://content.openalex.org/works/W2972809290.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W59997565","https://openalex.org/W1570411240","https://openalex.org/W1677182931","https://openalex.org/W1823409095","https://openalex.org/W1855112655","https://openalex.org/W1967983190","https://openalex.org/W1985940938","https://openalex.org/W2003357516","https://openalex.org/W2131763976","https://openalex.org/W2138913040","https://openalex.org/W2147101007","https://openalex.org/W2308200533","https://openalex.org/W2513853720","https://openalex.org/W2552737632","https://openalex.org/W2565565355","https://openalex.org/W2621826044","https://openalex.org/W2735894830","https://openalex.org/W2750384547","https://openalex.org/W2773853694","https://openalex.org/W2798878556","https://openalex.org/W2804484509","https://openalex.org/W2806498722","https://openalex.org/W2963206832","https://openalex.org/W2964115671","https://openalex.org/W2971713705"],"related_works":["https://openalex.org/W2954909726","https://openalex.org/W3139020568","https://openalex.org/W3126544799","https://openalex.org/W4313158102","https://openalex.org/W2905533880","https://openalex.org/W4313442939","https://openalex.org/W2971489542","https://openalex.org/W2995498660","https://openalex.org/W2088600489","https://openalex.org/W2736237086"],"abstract_inverted_index":{"Asynchronous":[0],"event-driven":[1],"computation":[2],"and":[3,22,88,107,111,129,161,177],"communication":[4],"using":[5],"spikes":[6],"facilitate":[7],"the":[8,30,39,53,108,122,171],"realization":[9],"of":[10,32,101,121],"spiking":[11,127,154],"neural":[12,48],"networks":[13,43,49,63],"(SNN)":[14],"to":[15,41,61,132],"be":[16],"massively":[17],"parallel,":[18],"extremely":[19],"energy":[20],"efficient":[21],"highly":[23],"robust":[24,35,67],"on":[25,174],"specialized":[26],"neuromorphic":[27,79,162],"hardware.":[28],"However,":[29],"lack":[31],"a":[33,133,140,157],"unified":[34],"learning":[36,143],"algorithm":[37,55,73,124,166,173],"limits":[38],"SNN":[40],"shallow":[42],"with":[44,126,180],"low":[45],"accuracies.":[46],"Artificial":[47],"(ANN),":[50],"however,":[51],"have":[52],"backpropagation":[54,72,123,165],"which":[56,64,138],"can":[57],"utilize":[58],"gradient":[59],"descent":[60],"train":[62],"are":[65],"locally":[66],"universal":[68],"function":[69],"approximators.":[70],"But":[71],"is":[74],"neither":[75],"biologically":[76,141,159],"plausible":[77,142,160],"nor":[78],"implementation":[80,163],"friendly":[81,164],"because":[82],"it":[83,131],"requires:":[84],"1)":[85],"separate":[86],"backward":[87,109],"forward":[89],"passes,":[90],"2)":[91],"differentiable":[92],"neurons,":[93],"3)":[94],"high-precision":[95],"propagated":[96],"errors,":[97],"4)":[98],"coherent":[99],"copy":[100],"weight":[102,114,135],"matrices":[103],"at":[104],"feedforward":[105],"weights":[106],"pass,":[110],"5)":[112],"non-local":[113],"update.":[115],"Thus,":[116],"we":[117],"propose":[118],"an":[119],"approximation":[120],"completely":[125],"neurons":[128,155],"extend":[130],"local":[134],"update":[136],"rule":[137,144],"resembles":[139],"spike-timing-dependent":[145],"plasticity":[146],"(STDP).":[147],"This":[148],"will":[149],"enable":[150],"error":[151],"propagation":[152],"through":[153],"for":[156,167],"more":[158],"SNNs.":[168],"We":[169],"test":[170],"proposed":[172],"various":[175],"traditional":[176],"non-traditional":[178],"benchmarks":[179],"competitive":[181],"results.":[182]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
