{"id":"https://openalex.org/W2735317938","doi":"https://doi.org/10.1109/ijcnn.2017.7966177","title":"EnsembleSNN: Distributed assistive STDP learning for energy-efficient recognition in spiking neural networks","display_name":"EnsembleSNN: Distributed assistive STDP learning for energy-efficient recognition in spiking neural networks","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2735317938","doi":"https://doi.org/10.1109/ijcnn.2017.7966177","mag":"2735317938"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7966177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5050310538","display_name":"Priyadarshini Panda","orcid":"https://orcid.org/0000-0002-4167-6782"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Priyadarshini Panda","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011164681","display_name":"Gopalakrishnan Srinivasan","orcid":"https://orcid.org/0000-0003-2015-8545"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gopalakrishnan Srinivasan","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031161187","display_name":"Kaushik Roy","orcid":"https://orcid.org/0009-0002-3375-2877"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaushik Roy","raw_affiliation_strings":["School of Electrical and Computer Engineering, Purdue University"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Purdue University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5050310538"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5734,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.6918711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2629","last_page":"2635"},"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":0.9984999895095825,"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":0.9983999729156494,"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/spiking-neural-network","display_name":"Spiking neural network","score":0.8958249688148499},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7918113470077515},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.7677879929542542},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.7280913591384888},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6063343286514282},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5603816509246826},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4829261004924774},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.469791054725647},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4388018846511841},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4386116564273834},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4292644262313843}],"concepts":[{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.8958249688148499},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7918113470077515},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7677879929542542},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.7280913591384888},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6063343286514282},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5603816509246826},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4829261004924774},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.469791054725647},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4388018846511841},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4386116564273834},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4292644262313843},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2017.7966177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.7900000214576721}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1486852018","https://openalex.org/W1534477342","https://openalex.org/W1570411240","https://openalex.org/W1604973310","https://openalex.org/W1966175794","https://openalex.org/W1967283371","https://openalex.org/W2016922062","https://openalex.org/W2018027127","https://openalex.org/W2021853834","https://openalex.org/W2033840893","https://openalex.org/W2038511109","https://openalex.org/W2108148142","https://openalex.org/W2112796928","https://openalex.org/W2121458485","https://openalex.org/W2130360162","https://openalex.org/W2138913040","https://openalex.org/W2155714768","https://openalex.org/W2214682759","https://openalex.org/W2399958287","https://openalex.org/W6682675057"],"related_works":["https://openalex.org/W4288417961","https://openalex.org/W4206030721","https://openalex.org/W2921035465","https://openalex.org/W2902404732","https://openalex.org/W4310427462","https://openalex.org/W2963468148","https://openalex.org/W4287671318","https://openalex.org/W2892732747","https://openalex.org/W3027014941","https://openalex.org/W3163379329"],"abstract_inverted_index":{"We":[0,97,131],"present":[1],"an":[2],"ensemble":[3],"approach":[4,134],"for":[5,16,25,139,169],"implementing":[6],"Spiking":[7],"Neural":[8],"Networks":[9],"(SNNs)":[10],"with":[11],"on-line":[12],"unsupervised":[13],"learning,":[14],"well-suited":[15],"robust":[17],"and":[18,162],"energy-efficient":[19],"design":[20],"of":[21,56,87,116,127,142,166],"neuromorphic":[22],"computing":[23],"systems":[24],"pattern":[26],"recognition":[27],"tasks.":[28],"Inspired":[29],"from":[30,72,84],"the":[31,37,40,57,63,68,74,88,94,113,117,123,128,136,158],"collective":[32,82,124],"neuronal":[33],"activity":[34],"observed":[35],"in":[36,51,60,150],"visual":[38],"cortex,":[39],"proposed":[41,129],"EnsembleSNN":[42,89],"architecture":[43],"involves":[44],"multiple":[45],"simple":[46],"SNNs":[47],"or":[48],"ensembles":[49,86,103,107],"acting":[50],"parallel":[52],"on":[53,135],"different":[54,77,102,140],"aspects":[55],"input.":[58],"This":[59],"turn":[61],"reduces":[62],"training":[64],"complexity":[65],"due":[66],"to":[67,92,108],"decreased":[69],"connectivity":[70],"obtained":[71],"decomposing":[73],"input":[75,118],"across":[76,101],"ensembles.":[78],"During":[79],"inference,":[80],"a":[81],"decision":[83,125],"all":[85],"is":[90],"considered":[91],"obtain":[93],"final":[95],"prediction.":[96],"add":[98],"predictive":[99],"connections":[100],"that":[104,120],"enables":[105],"individual":[106],"learn":[109],"some":[110],"statistics":[111],"about":[112],"remaining":[114],"portions":[115],"image":[119],"further":[121],"enhances":[122],"making":[126],"architecture.":[130],"evaluate":[132],"our":[133],"MNIST":[137],"dataset":[138],"configurations":[141],"EnsembleSNN.":[143],"Our":[144],"experiments":[145],"demonstrate":[146],"upto":[147,167],"2.8x":[148],"improvement":[149],"efficiency,":[151],"while":[152],"yielding":[153],"better":[154],"(~2.5%)":[155],"accuracy":[156,171],"than":[157],"optimized":[159],"baseline":[160],"network,":[161],"even":[163],"higher":[164],"improvements":[165],"3.7x":[168],"minimal":[170],"degradation":[172],"(~3.2%).":[173]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}