{"id":"https://openalex.org/W7162689444","doi":"https://doi.org/10.48550/arxiv.2605.27412","title":"Advancing Direct Training for Spiking Neural Networks with Circulate-Firing Neurons and Learnable Gradients","display_name":"Advancing Direct Training for Spiking Neural Networks with Circulate-Firing Neurons and Learnable Gradients","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7162689444","doi":"https://doi.org/10.48550/arxiv.2605.27412"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27412","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27412","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.27412","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137230044","display_name":"Feifan Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Feifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137258149","display_name":"Xiang Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137273132","display_name":"Yang Liu","orcid":"https://orcid.org/0009-0000-1222-4827"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137252827","display_name":"Qiang Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Qiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.8702999949455261,"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":0.8702999949455261,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.06989999860525131,"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/T12029","display_name":"DNA and Biological Computing","score":0.006000000052154064,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.7861999869346619},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6172000169754028},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5787000060081482},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.43309998512268066},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.41110000014305115},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.3736000061035156},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.3684999942779541}],"concepts":[{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.7861999869346619},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6899999976158142},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6536999940872192},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6172000169754028},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5787000060081482},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.43309998512268066},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4278999865055084},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.3736000061035156},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3684999942779541},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C115680565","wikidata":"https://www.wikidata.org/wiki/Q5977448","display_name":"Gradient method","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.2660999894142151},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C87868495","wikidata":"https://www.wikidata.org/wiki/Q750843","display_name":"Information processing","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27412","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27412","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.27412","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27412","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8544363975524902}],"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":[1,19],"Networks":[2,20],"(SNNs)":[3],"have":[4],"emerged":[5],"with":[6,77],"promising":[7],"energy-efficient":[8],"property,":[9],"yet":[10],"a":[11,72,82,99,112,176],"substantial":[12],"performance":[13,139,171],"gap":[14,23],"persists":[15],"compared":[16],"to":[17,57,117,148],"Artificial":[18],"(ANNs).":[21],"This":[22],"stems":[24],"from":[25],"at":[26],"least":[27],"two":[28,68],"key":[29],"limitations:":[30],"first,":[31,81],"conventional":[32],"spiking":[33,84,182],"neurons":[34],"offer":[35],"limited":[36],"information":[37,89],"representation":[38,90],"capacity,":[39],"underutilizing":[40],"the":[41,160],"rich":[42],"dynamics":[43,167],"of":[44,151,162,168],"membrane":[45,94,124,166],"potentials;":[46],"second,":[47,98],"fixed":[48],"surrogate":[49,102],"gradient":[50,59,103,107],"(SG)":[51],"functions":[52],"across":[53,140],"time":[54],"steps":[55],"leads":[56],"imprecise":[58],"propagation,":[60],"impeding":[61],"effective":[62],"direct":[63,74],"training.":[64],"To":[65],"address":[66],"these":[67],"challenges,":[69],"we":[70],"propose":[71],"new":[73,177],"training":[75],"algorithm":[76],"three":[78],"core":[79],"innovations:":[80],"circulate-firing":[83],"neuron":[85],"model":[86],"that":[87,134],"enhances":[88],"capacity":[91],"by":[92],"leveraging":[93],"potentials":[95,125],"more":[96],"effectively;":[97],"time-step-wise":[100],"learnable":[101],"function,":[104],"enabling":[105],"accurate":[106],"estimation":[108],"during":[109],"backpropagation;":[110],"third,":[111],"positive-negative":[113],"balanced":[114],"loss":[115],"function":[116],"achieve":[118,137],"equilibrium":[119],"between":[120],"positive":[121],"and":[122,126,173],"negative":[123],"further":[127,163],"boost":[128],"SNN":[129],"performance.":[130],"Extensive":[131],"experiments":[132],"demonstrate":[133],"our":[135],"methods":[136,144],"competitive":[138],"multiple":[141],"datasets.":[142],"Our":[143,157],"can":[145],"generalize":[146],"seamlessly":[147],"advanced":[149],"architectures":[150],"Transformer,":[152],"consistently":[153],"outperforming":[154],"existing":[155],"methods.":[156],"work":[158],"highlights":[159],"effectiveness":[161],"harnessing":[164],"intrinsic":[165],"SNNs":[169],"for":[170,179],"improvement,":[172],"thus":[174],"open":[175],"avenue":[178],"advancing":[180],"high-performance":[181],"neural":[183],"architectures.":[184]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-29T00:00:00"}