{"id":"https://openalex.org/W4384024675","doi":"https://doi.org/10.1162/neco_a_01604","title":"A Noise-Based Novel Strategy for Faster SNN Training","display_name":"A Noise-Based Novel Strategy for Faster SNN Training","publication_year":2023,"publication_date":"2023-07-12","ids":{"openalex":"https://openalex.org/W4384024675","doi":"https://doi.org/10.1162/neco_a_01604","pmid":"https://pubmed.ncbi.nlm.nih.gov/37437192"},"language":"en","primary_location":{"id":"doi:10.1162/neco_a_01604","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_01604","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718","https://openalex.org/P4310316440"],"host_organization_lineage_names":["The MIT Press","Massachusetts Institute of Technology"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5102712449","display_name":"Chunming Jiang","orcid":"https://orcid.org/0000-0002-5934-6570"},"institutions":[{"id":"https://openalex.org/I185492890","display_name":"University of Canterbury","ror":"https://ror.org/03y7q9t39","country_code":"NZ","type":"education","lineage":["https://openalex.org/I185492890"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Chunming Jiang","raw_affiliation_strings":["Department of Mechanical Engineering, University of Canterbury, Canterbury CT2 7NX, New Zealand cji39@uclive.ac.nz"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, University of Canterbury, Canterbury CT2 7NX, New Zealand cji39@uclive.ac.nz","institution_ids":["https://openalex.org/I185492890"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100694323","display_name":"Yilei Zhang","orcid":"https://orcid.org/0000-0002-5694-8699"},"institutions":[{"id":"https://openalex.org/I185492890","display_name":"University of Canterbury","ror":"https://ror.org/03y7q9t39","country_code":"NZ","type":"education","lineage":["https://openalex.org/I185492890"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Yilei Zhang","raw_affiliation_strings":["Department of Mechanical Engineering, University of Canterbury, Canterbury CT2 7NX, New Zealand yilei.zhang@canterbury.ac.nz"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, University of Canterbury, Canterbury CT2 7NX, New Zealand yilei.zhang@canterbury.ac.nz","institution_ids":["https://openalex.org/I185492890"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100694323"],"corresponding_institution_ids":["https://openalex.org/I185492890"],"apc_list":null,"apc_paid":null,"fwci":0.4189,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.54615567,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"35","issue":"9","first_page":"1593","last_page":"1608"},"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":0.9987999796867371,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9987999796867371,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.79622483253479},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7737807631492615},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6344941854476929},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.628637433052063},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6078634262084961},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5430019497871399},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.4494587182998657},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4410872757434845},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.42916160821914673},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39613115787506104},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3440972566604614},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.06951165199279785}],"concepts":[{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.79622483253479},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7737807631492615},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6344941854476929},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.628637433052063},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6078634262084961},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5430019497871399},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.4494587182998657},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4410872757434845},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.42916160821914673},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39613115787506104},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3440972566604614},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.06951165199279785},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1162/neco_a_01604","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_01604","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718","https://openalex.org/P4310316440"],"host_organization_lineage_names":["The MIT Press","Massachusetts Institute of Technology"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},{"id":"pmid:37437192","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37437192","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural computation","raw_type":"Journal Article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1570411240","https://openalex.org/W1572995516","https://openalex.org/W1645800954","https://openalex.org/W2007815184","https://openalex.org/W2020629162","https://openalex.org/W2020676607","https://openalex.org/W2110654393","https://openalex.org/W2115831804","https://openalex.org/W2154616847","https://openalex.org/W2162827630","https://openalex.org/W2164042265","https://openalex.org/W2165639766","https://openalex.org/W2233731247","https://openalex.org/W2240536489","https://openalex.org/W2569813014","https://openalex.org/W2620474507","https://openalex.org/W2621826044","https://openalex.org/W2775079417","https://openalex.org/W2798878556","https://openalex.org/W2892077605","https://openalex.org/W2903271863","https://openalex.org/W2963263347","https://openalex.org/W2964338223","https://openalex.org/W2984844508","https://openalex.org/W2990793844","https://openalex.org/W3007283957","https://openalex.org/W3023721287","https://openalex.org/W3035000326","https://openalex.org/W3035644810","https://openalex.org/W3098917398","https://openalex.org/W3102087395","https://openalex.org/W3126711481","https://openalex.org/W3135613337","https://openalex.org/W4212774754","https://openalex.org/W4240177607","https://openalex.org/W6631190155","https://openalex.org/W6634165055","https://openalex.org/W6689286705","https://openalex.org/W6726497184","https://openalex.org/W6739434765","https://openalex.org/W6771742384","https://openalex.org/W6774150660","https://openalex.org/W6790089871"],"related_works":["https://openalex.org/W4239286941","https://openalex.org/W2088845016","https://openalex.org/W589102260","https://openalex.org/W1966421350","https://openalex.org/W1868434454","https://openalex.org/W4366985237","https://openalex.org/W3042419602","https://openalex.org/W2966649771","https://openalex.org/W4287203102","https://openalex.org/W3153981999"],"abstract_inverted_index":{"Spiking":[0],"neural":[1,28,109],"networks":[2],"(SNNs)":[3],"are":[4],"receiving":[5],"increasing":[6],"attention":[7],"due":[8],"to":[9,48,122,136,165,177],"their":[10,77,161],"low":[11],"power":[12],"consumption":[13],"and":[14,32,39,74,154,179],"strong":[15],"bioplausibility.":[16],"Optimization":[17],"of":[18,52,58,69,94,131,157],"SNNs":[19,65,158],"is":[20],"a":[21,44,85,101,123,137],"challenging":[22],"task.":[23],"Two":[24],"main":[25],"methods,":[26,169],"artificial":[27],"network":[29],"(ANN)-to-SNN":[30],"conversion":[31,42],"spike-based":[33,61],"backpropagation":[34],"(BP),":[35],"both":[36],"have":[37],"advantages":[38],"limitations.":[40],"ANN-to-SNN":[41],"requires":[43],"long":[45],"inference":[46,155,186],"time":[47,75,174],"approximate":[49],"the":[50,56,92,95,108,117,152,166,192],"accuracy":[51,141],"ANN,":[53],"thus":[54],"diminishing":[55],"benefits":[57,93],"SNN.":[59],"With":[60],"BP,":[62],"training":[63,88,153,173],"high-precision":[64],"typically":[66],"consumes":[67],"dozens":[68],"times":[70,156,184],"more":[71,181,200],"computational":[72],"resources":[73],"than":[76,182],"ANN":[78],"counterparts.":[79],"In":[80],"this":[81],"letter,":[82],"we":[83],"propose":[84],"novel":[86],"SNN":[87],"approach":[89],"that":[90,147,191],"combines":[91],"two":[96,168],"methods.":[97],"We":[98,188],"first":[99],"train":[100],"single-step":[102,118],"SNN(T":[103,119,125],"=":[104,120,126],"1)":[105,121],"by":[106,175],"approximating":[107],"potential":[110],"distribution":[111],"with":[112,196],"random":[113],"noise,":[114],"then":[115],"convert":[116],"multistep":[124],"N)":[127],"losslessly.":[128],"The":[129,144],"introduction":[130],"gaussian":[132],"distributed":[133],"noise":[134,197],"leads":[135],"significant":[138],"gain":[139],"in":[140],"after":[142],"conversion.":[143],"results":[145],"show":[146],"our":[148],"method":[149],"considerably":[150],"reduces":[151],"while":[159],"maintaining":[160],"high":[162],"accuracy.":[163],"Compared":[164],"previous":[167],"ours":[170],"can":[171],"reduce":[172],"65%":[176],"75%":[178],"achieves":[180],"100":[183],"faster":[185],"speed.":[187],"also":[189],"argue":[190],"neuron":[193],"model":[194],"augmented":[195],"makes":[198],"it":[199],"bioplausible.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
