{"id":"https://openalex.org/W7138012347","doi":"https://doi.org/10.1609/aaai.v40i3.37206","title":"Generalized Threshold Optimization with Harmony Multi-Threshold Neurons for Accurate ANN-to-SNN Conversion","display_name":"Generalized Threshold Optimization with Harmony Multi-Threshold Neurons for Accurate ANN-to-SNN Conversion","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138012347","doi":"https://doi.org/10.1609/aaai.v40i3.37206"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i3.37206","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i3.37206","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i3.37206","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101992901","display_name":"Wenhan Zhang","orcid":"https://orcid.org/0000-0002-3793-2157"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wenhan Zhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051036720","display_name":"Zihan Huang","orcid":"https://orcid.org/0000-0002-5781-4166"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zihan Huang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129752159","display_name":"Tong Bu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tong Bu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129672887","display_name":"Tiejun Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tiejun Huang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129679305","display_name":"Zhaofei Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhaofei Yu","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101992901"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36486486,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"3","first_page":"2227","last_page":"2235"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.892300009727478,"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":0.892300009727478,"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":0.047600001096725464,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.014499999582767487,"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/benchmark","display_name":"Benchmark (surveying)","score":0.6252999901771545},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5742999911308289},{"id":"https://openalex.org/keywords/harmony-search","display_name":"Harmony search","score":0.499099999666214},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4675999879837036},{"id":"https://openalex.org/keywords/energy-transformation","display_name":"Energy transformation","score":0.42879998683929443},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.4074999988079071},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.39820000529289246},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.3806000053882599}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6473000049591064},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6252999901771545},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5742999911308289},{"id":"https://openalex.org/C33099171","wikidata":"https://www.wikidata.org/wiki/Q26208718","display_name":"Harmony search","level":2,"score":0.499099999666214},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4675999879837036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4625999927520752},{"id":"https://openalex.org/C144822601","wikidata":"https://www.wikidata.org/wiki/Q11271324","display_name":"Energy transformation","level":2,"score":0.42879998683929443},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.4074999988079071},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.39820000529289246},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3806000053882599},{"id":"https://openalex.org/C2776990819","wikidata":"https://www.wikidata.org/wiki/Q177058","display_name":"Artificial neuron","level":3,"score":0.3643999993801117},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34540000557899475},{"id":"https://openalex.org/C186565885","wikidata":"https://www.wikidata.org/wiki/Q1651163","display_name":"Biological neuron model","level":3,"score":0.33640000224113464},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3248000144958496},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2775000035762787},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.26829999685287476},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.26649999618530273}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i3.37206","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i3.37206","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i3.37206","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i3.37206","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.9117005467414856,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Spiking":[0],"Neural":[1,31],"Networks(SNNs)":[2],"are":[3],"a":[4,38,65,106,112],"promising":[5],"paradigm":[6],"designed":[7],"to":[8,25,80,97,111,119,120],"emulate":[9],"the":[10,16,50,59,122,142],"brain's":[11],"energy":[12,147],"efficient":[13,23],"by":[14,49],"incorporating":[15],"timing":[17],"of":[18,53,116],"spikes.":[19],"Conversion":[20],"is":[21,46,88],"an":[22,92],"way":[24],"obtain":[26],"high-performance":[27],"SNNs":[28],"from":[29],"Artificial":[30],"Networks(ANNs).":[32],"Existing":[33],"conversion":[34,60],"methods":[35],"often":[36],"face":[37],"trade-off":[39],"between":[40],"accuracy":[41,134],"and":[42,146],"time":[43,144],"steps,":[44],"which":[45,76],"largely":[47],"caused":[48],"incomplete":[51],"release":[52],"residual":[54,82],"membrane":[55,83],"potentials.":[56,84],"To":[57],"minimize":[58,81],"error,":[61],"this":[62],"paper":[63],"proposed":[64,86],"harmonious":[66],"mathematical":[67],"property-based":[68],"neuron,":[69],"called":[70],"Harmony":[71],"Multi-Threshold":[72],"Neurons":[73],"(H-MT":[74],"Neuron),":[75],"utilizes":[77],"multiple":[78],"spikes":[79],"The":[85],"neuron":[87,124],"further":[89],"enhanced":[90],"with":[91],"optional":[93],"effective":[94],"communication":[95],"mechanism":[96],"achieve":[98,132],"more":[99],"accurate":[100],"conversion.":[101],"In":[102],"addition,":[103],"we":[104],"propose":[105],"threshold":[107],"optimization":[108],"method":[109,131],"applicable":[110],"broader":[113],"range":[114],"cases":[115],"spiking":[117],"neurons":[118],"find":[121],"optimal":[123],"thresholds.":[125],"Experiment":[126],"results":[127],"demonstrate":[128],"that":[129],"our":[130],"superior":[133],"on":[135],"ImageNet":[136],"benchmark":[137],"datasets":[138],"while":[139],"significantly":[140],"reducing":[141],"required":[143],"steps":[145],"consumption.":[148]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}
