{"id":"https://openalex.org/W3105490104","doi":"https://doi.org/10.1109/tnnls.2021.3073016","title":"LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing","display_name":"LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing","publication_year":2021,"publication_date":"2021-05-12","ids":{"openalex":"https://openalex.org/W3105490104","doi":"https://doi.org/10.1109/tnnls.2021.3073016","mag":"3105490104","pmid":"https://pubmed.ncbi.nlm.nih.gov/33979292"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2021.3073016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3073016","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"preprint","indexed_in":["crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2011.06176","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039331830","display_name":"Zhenzhi Wu","orcid":"https://orcid.org/0000-0002-7859-7818"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhenzhi Wu","raw_affiliation_strings":["Lynxi Technologies Company Ltd., Beijing, China","[Lynxi Technologies Company Ltd., Beijing 100080, China (e-mail: zhenzhi.wu@lynxi.com)]"],"affiliations":[{"raw_affiliation_string":"Lynxi Technologies Company Ltd., Beijing, China","institution_ids":[]},{"raw_affiliation_string":"[Lynxi Technologies Company Ltd., Beijing 100080, China (e-mail: zhenzhi.wu@lynxi.com)]","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102853724","display_name":"Hehui Zhang","orcid":"https://orcid.org/0000-0002-7164-6138"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hehui Zhang","raw_affiliation_strings":["Lynxi Technologies Company Ltd., Beijing, China","[Lynxi Technologies Company Ltd., Beijing 100080, China, and also with the Beijing University of Posts and Telecommunications, Beijing 100876, China.]"],"affiliations":[{"raw_affiliation_string":"Lynxi Technologies Company Ltd., Beijing, China","institution_ids":[]},{"raw_affiliation_string":"[Lynxi Technologies Company Ltd., Beijing 100080, China, and also with the Beijing University of Posts and Telecommunications, Beijing 100876, China.]","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101582543","display_name":"Yihan Lin","orcid":"https://orcid.org/0000-0002-0717-034X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihan Lin","raw_affiliation_strings":["Department of Precision Instrument, Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Precision Instrument, Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018970859","display_name":"Guoqi Li","orcid":"https://orcid.org/0000-0002-8994-431X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoqi Li","raw_affiliation_strings":["Department of Precision Instrument, Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Precision Instrument, Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046194584","display_name":"Meng Wang","orcid":"https://orcid.org/0000-0002-2829-1976"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Wang","raw_affiliation_strings":["Lynxi Technologies Company Ltd., Beijing, China","[Lynxi Technologies Company Ltd., Beijing 100080, China, and also with the Beijing University of Posts and Telecommunications, Beijing 100876, China.]"],"affiliations":[{"raw_affiliation_string":"Lynxi Technologies Company Ltd., Beijing, China","institution_ids":[]},{"raw_affiliation_string":"[Lynxi Technologies Company Ltd., Beijing 100080, China, and also with the Beijing University of Posts and Telecommunications, Beijing 100876, China.]","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082349626","display_name":"Ye Tang","orcid":"https://orcid.org/0000-0002-2205-2229"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Tang","raw_affiliation_strings":["Lynxi Technologies Company Ltd., Beijing, China","[Lynxi Technologies Company Ltd., Beijing 100080, China, and also with the Beijing University of Posts and Telecommunications, Beijing 100876, China.]"],"affiliations":[{"raw_affiliation_string":"Lynxi Technologies Company Ltd., Beijing, China","institution_ids":[]},{"raw_affiliation_string":"[Lynxi Technologies Company Ltd., Beijing 100080, China, and also with the Beijing University of Posts and Telecommunications, Beijing 100876, China.]","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5039331830"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0159,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.74901409,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"33","issue":"11","first_page":"6249","last_page":"6262"},"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":0.9970999956130981,"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":0.9955999851226807,"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/computer-science","display_name":"Computer science","score":0.817808210849762},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.6594828963279724},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.6355668306350708},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5959007143974304},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5670433640480042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49481338262557983},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.42924290895462036},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.405019611120224}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.817808210849762},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.6594828963279724},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.6355668306350708},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5959007143974304},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5670433640480042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49481338262557983},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.42924290895462036},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.405019611120224},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D003071","descriptor_name":"Cognition","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003071","descriptor_name":"Cognition","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003071","descriptor_name":"Cognition","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009474","descriptor_name":"Neurons","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":true},{"descriptor_ui":"D009474","descriptor_name":"Neurons","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":true},{"descriptor_ui":"D009474","descriptor_name":"Neurons","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":4,"locations":[{"id":"doi:10.1109/tnnls.2021.3073016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3073016","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:33979292","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33979292","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":"IEEE transactions on neural networks and learning systems","raw_type":null},{"id":"mag:3105490104","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2011.06176","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2011.06176","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2011.06176","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"mag:3105490104","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2011.06176","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8999999761581421,"display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G4123105846","display_name":null,"funder_award_id":"2018YFE0200200","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G7282031291","display_name":null,"funder_award_id":"2018AAA0102604","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G7549707144","display_name":null,"funder_award_id":"61876215","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320311687","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322392","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":77,"referenced_works":["https://openalex.org/W101771737","https://openalex.org/W1485009520","https://openalex.org/W1512746852","https://openalex.org/W1525961042","https://openalex.org/W1570411240","https://openalex.org/W1604973310","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1904365287","https://openalex.org/W1924770834","https://openalex.org/W1969456244","https://openalex.org/W1976909436","https://openalex.org/W2006370340","https://openalex.org/W2006487439","https://openalex.org/W2007431958","https://openalex.org/W2037030438","https://openalex.org/W2063695057","https://openalex.org/W2064675550","https://openalex.org/W2108598243","https://openalex.org/W2113081107","https://openalex.org/W2138913040","https://openalex.org/W2157331557","https://openalex.org/W2163605009","https://openalex.org/W2165061373","https://openalex.org/W2165396124","https://openalex.org/W2194775991","https://openalex.org/W2207801947","https://openalex.org/W2469278928","https://openalex.org/W2507540959","https://openalex.org/W2513853720","https://openalex.org/W2562377059","https://openalex.org/W2619510810","https://openalex.org/W2621826044","https://openalex.org/W2733297609","https://openalex.org/W2745933219","https://openalex.org/W2783525259","https://openalex.org/W2796051767","https://openalex.org/W2796323669","https://openalex.org/W2808053377","https://openalex.org/W2884121069","https://openalex.org/W2892077605","https://openalex.org/W2909235141","https://openalex.org/W2922107638","https://openalex.org/W2923485687","https://openalex.org/W2949117887","https://openalex.org/W2951534927","https://openalex.org/W2963335874","https://openalex.org/W2963510238","https://openalex.org/W2963760575","https://openalex.org/W2963986592","https://openalex.org/W2964031418","https://openalex.org/W2964035021","https://openalex.org/W2966081953","https://openalex.org/W2974328520","https://openalex.org/W2976743137","https://openalex.org/W2979969178","https://openalex.org/W2984844508","https://openalex.org/W2990793844","https://openalex.org/W2998412226","https://openalex.org/W3007283957","https://openalex.org/W3019438330","https://openalex.org/W3022766987","https://openalex.org/W3091431973","https://openalex.org/W3102750118","https://openalex.org/W4285719527","https://openalex.org/W6631399359","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6640036494","https://openalex.org/W6640212811","https://openalex.org/W6713134421","https://openalex.org/W6730823560","https://openalex.org/W6739879593","https://openalex.org/W6756040250","https://openalex.org/W6774150660","https://openalex.org/W6780226713","https://openalex.org/W6947911215"],"related_works":["https://openalex.org/W2963186210","https://openalex.org/W3185489689","https://openalex.org/W3211535359","https://openalex.org/W2963887857","https://openalex.org/W3091431973","https://openalex.org/W2972429589","https://openalex.org/W2949079724","https://openalex.org/W3174064989","https://openalex.org/W3022089980","https://openalex.org/W3025703747","https://openalex.org/W3183450825","https://openalex.org/W3036270242","https://openalex.org/W2913050285","https://openalex.org/W3161867321","https://openalex.org/W2610920143","https://openalex.org/W3045879661","https://openalex.org/W2883947307","https://openalex.org/W2020676607","https://openalex.org/W3109080038","https://openalex.org/W1866834877"],"abstract_inverted_index":{"Spiking":[0],"neural":[1,142],"networks":[2,228],"(SNNs)":[3],"based":[4],"on":[5,90,191,201,250],"the":[6,25,39,49,97,102,113,149,163,181,261],"leaky":[7,65],"integrate":[8,66,120],"and":[9,19,29,67,82,186,197,212,222,266,270],"fire":[10,69],"(LIF)":[11],"model":[12,72],"have":[13],"been":[14],"applied":[15],"to":[16,24,106,119,169,180],"energy-efficient":[17],"temporal":[18,98,109],"spatiotemporal":[20,94,132,202,272],"processing":[21,110],"tasks.":[22],"Due":[23],"bioplausible":[26],"neuronal":[27],"dynamics":[28,105],"simplicity,":[30],"LIF-SNN":[31],"benefits":[32],"from":[33],"event-driven":[34],"processing,":[35],"however,":[36],"usually":[37],"face":[38],"embarrassment":[40],"of":[41,219,263],"reduced":[42],"performance.":[43],"This":[44],"may":[45],"because,":[46],"in":[47,59],"LIF-SNN,":[48,243],"neurons":[50],"transmit":[51],"information":[52,122,273],"via":[53],"spikes.":[54],"To":[55],"address":[56],"this":[57,60],"issue,":[58],"work,":[61],"we":[62],"propose":[63],"a":[64,83,131,258],"analog":[68,75],"(LIAF)":[70],"neuron":[71],"so":[73],"that":[74,175],"values":[76],"can":[77,135,152],"be":[78,137,153],"transmitted":[79],"among":[80],"neurons,":[81],"deep":[84],"network":[85,143,151],"termed":[86],"LIAF-Net":[87,176,244,256],"is":[88,117],"built":[89,150,229],"it":[91],"for":[92,268],"efficient":[93,271],"processing.":[95,274],"In":[96,112,147,254],"domain,":[99,115],"LIAF":[100,116,134],"follows":[101],"traditional":[103,140,227,242],"LIF":[104],"maintain":[107],"its":[108],"capability.":[111],"spatial":[114,121],"able":[118],"through":[123,157],"convolutional":[124,233],"integration":[125],"or":[126,236],"fully":[127],"connected":[128],"integration.":[129],"As":[130],"layer,":[133],"also":[136,245],"used":[138],"with":[139,155,215,226,241],"artificial":[141],"(ANN)":[144],"layers":[145],"jointly.":[146],"addition,":[148],"trained":[154],"backpropagation":[156],"time":[158],"(BPTT)":[159],"directly,":[160],"which":[161],"avoids":[162],"performance":[164,179,200],"loss":[165],"caused":[166],"by":[167,230],"ANN":[168],"SNN":[170],"conversion.":[171],"Experiment":[172],"results":[173],"indicate":[174],"achieves":[177,198],"comparable":[178],"gated":[182],"recurrent":[183],"unit":[184],"(GRU)":[185],"long":[187],"short-term":[188],"memory":[189],"(LSTM)":[190],"bAbI":[192],"question":[193],"answering":[194],"(QA)":[195],"tasks":[196],"state-of-the-art":[199],"dynamic":[203],"vision":[204],"sensor":[205],"(DVS)":[206],"data":[207],"sets,":[208],"including":[209],"MNIST-DVS,":[210],"CIFAR10-DVS,":[211],"DVS128":[213],"Gesture,":[214],"much":[216],"less":[217],"number":[218],"synaptic":[220],"weights":[221],"computational":[223],"overhead":[224],"compared":[225],"LSTM,":[231],"GRU,":[232],"LSTM":[234],"(ConvLSTM),":[235],"3-D":[237],"convolution":[238],"(Conv3D).":[239],"Compared":[240],"shows":[246],"dramatic":[247],"accuracy":[248],"gain":[249],"all":[251],"these":[252],"experiments.":[253],"conclusion,":[255],"provides":[257],"framework":[259],"combining":[260],"advantages":[262],"both":[264],"ANNs":[265],"SNNs":[267],"lightweight":[269]},"counts_by_year":[{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}