{"id":"https://openalex.org/W7124769909","doi":"https://doi.org/10.48550/arxiv.2601.11261","title":"Effects of Introducing Synaptic Scaling on Spiking Neural Network Learning","display_name":"Effects of Introducing Synaptic Scaling on Spiking Neural Network Learning","publication_year":2026,"publication_date":"2026-01-16","ids":{"openalex":"https://openalex.org/W7124769909","doi":"https://doi.org/10.48550/arxiv.2601.11261"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.11261","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.11261","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.2601.11261","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121757928","display_name":"Shinnosuke Touda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Touda, Shinnosuke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103440526","display_name":"Hirotsugu Okuno","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Okuno, Hirotsugu","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":0.9513999819755554,"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":0.9513999819755554,"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":0.012799999676644802,"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":0.010900000110268593,"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/mnist-database","display_name":"MNIST database","score":0.8733000159263611},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.7106000185012817},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.641700029373169},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.6000000238418579},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.5020999908447266},{"id":"https://openalex.org/keywords/synaptic-plasticity","display_name":"Synaptic plasticity","score":0.5006999969482422},{"id":"https://openalex.org/keywords/learning-rule","display_name":"Learning rule","score":0.4440000057220459},{"id":"https://openalex.org/keywords/synaptic-scaling","display_name":"Synaptic scaling","score":0.4244999885559082}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8733000159263611},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.7106000185012817},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6732000112533569},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6575999855995178},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.641700029373169},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6000000238418579},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.5020999908447266},{"id":"https://openalex.org/C98229152","wikidata":"https://www.wikidata.org/wiki/Q1551556","display_name":"Synaptic plasticity","level":3,"score":0.5006999969482422},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4569000005722046},{"id":"https://openalex.org/C2779127903","wikidata":"https://www.wikidata.org/wiki/Q6510194","display_name":"Learning rule","level":3,"score":0.4440000057220459},{"id":"https://openalex.org/C117718741","wikidata":"https://www.wikidata.org/wiki/Q7662041","display_name":"Synaptic scaling","level":5,"score":0.4244999885559082},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4180999994277954},{"id":"https://openalex.org/C66949984","wikidata":"https://www.wikidata.org/wiki/Q7662043","display_name":"Synaptic weight","level":3,"score":0.349700003862381},{"id":"https://openalex.org/C5687787","wikidata":"https://www.wikidata.org/wiki/Q5889850","display_name":"Homeostatic plasticity","level":5,"score":0.34200000762939453},{"id":"https://openalex.org/C159919123","wikidata":"https://www.wikidata.org/wiki/Q7577157","display_name":"Spike-timing-dependent plasticity","level":4,"score":0.32820001244544983},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C120822770","wikidata":"https://www.wikidata.org/wiki/Q5156355","display_name":"Competitive learning","level":3,"score":0.27399998903274536},{"id":"https://openalex.org/C187782996","wikidata":"https://www.wikidata.org/wiki/Q9390548","display_name":"Neural ensemble","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C79186407","wikidata":"https://www.wikidata.org/wiki/Q472074","display_name":"Plasticity","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C112592302","wikidata":"https://www.wikidata.org/wiki/Q1207387","display_name":"Excitatory postsynaptic potential","level":3,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.11261","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.11261","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.2601.11261","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.11261","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":[],"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":[1,10,33,64],"networks":[2],"(SNNs)":[3],"employing":[4],"unsupervised":[5],"learning":[6,45],"methods":[7],"inspired":[8],"by":[9],"plasticity":[11,38,65],"are":[12],"expected":[13],"to":[14,99,139],"be":[15],"a":[16,47,57],"new":[17],"framework":[18],"for":[19,76],"artificial":[20],"intelligence.":[21],"In":[22],"this":[23],"study,":[24],"we":[25],"investigated":[26],"the":[27,44,71,82,86,92,111,115,129,141,150,157],"effect":[28],"of":[29,32,52,63,84,89,131,146,163],"multiple":[30,61],"types":[31,62],"plasticity,":[34],"such":[35],"as":[36],"spike-time-dependent":[37],"(STDP)":[39],"and":[40,70,78,91,127,136,153],"synaptic":[41,97,107,125],"scaling,":[42],"on":[43,110,149,156],"in":[46,96,118,133],"winner-take-all":[48],"(WTA)":[49],"network":[50,59,142],"composed":[51],"spiking":[53],"neurons.":[54],"We":[55,80],"implemented":[56],"WTA":[58],"with":[60],"using":[66],"Python.":[67],"The":[68,103],"MNIST":[69,151],"Fashion-MNIST":[72,158],"datasets":[73],"were":[74],"used":[75,95],"training":[77],"testing.":[79],"varied":[81],"number":[83,130],"neurons,":[85],"time":[87],"constant":[88],"STDP,":[90],"normalization":[93],"method":[94],"scaling":[98,108,126],"compare":[100],"classification":[101,120,144],"accuracy.":[102],"results":[104],"demonstrated":[105],"that":[106],"based":[109],"L2":[112],"norm":[113],"was":[114],"most":[116],"effective":[117],"improving":[119],"performance.":[121],"By":[122],"implementing":[123],"L2-norm-based":[124],"setting":[128],"neurons":[132],"both":[134],"excitatory":[135],"inhibitory":[137],"layers":[138],"400,":[140],"achieved":[143],"accuracies":[145],"88.84":[147],"%":[148,155],"dataset":[152,159],"68.01":[154],"after":[160],"one":[161],"epoch":[162],"training.":[164]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-01-20T00:00:00"}