{"id":"https://openalex.org/W7161701422","doi":"https://doi.org/10.48550/arxiv.2605.17686","title":"Brain-inspired spike-timing plasticity for reliable label-efficient event-camera vision","display_name":"Brain-inspired spike-timing plasticity for reliable label-efficient event-camera vision","publication_year":2026,"publication_date":"2026-05-17","ids":{"openalex":"https://openalex.org/W7161701422","doi":"https://doi.org/10.48550/arxiv.2605.17686"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.17686","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17686","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.17686","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130481747","display_name":"Mohamad Yazan Sadoun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sadoun, Mohamad Yazan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128194497","display_name":"Sarah Sharif","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sharif, Sarah","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5121501701","display_name":"Yaser Mike Banad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Banad, Yaser Mike","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":0.9537000060081482,"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":0.9537000060081482,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.01769999973475933,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.011599999852478504,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/detector","display_name":"Detector","score":0.7003999948501587},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.3571000099182129},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.3547999858856201},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.3167000114917755},{"id":"https://openalex.org/keywords/plasticity","display_name":"Plasticity","score":0.30559998750686646},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.30309998989105225},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2856999933719635}],"concepts":[{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.7003999948501587},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6758999824523926},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42969998717308044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42410001158714294},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3714999854564667},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3571000099182129},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.31049999594688416},{"id":"https://openalex.org/C79186407","wikidata":"https://www.wikidata.org/wiki/Q472074","display_name":"Plasticity","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.30309998989105225},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C138101251","wikidata":"https://www.wikidata.org/wiki/Q213092","display_name":"Thread (computing)","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C149810388","wikidata":"https://www.wikidata.org/wiki/Q5374873","display_name":"Emulation","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C131017901","wikidata":"https://www.wikidata.org/wiki/Q170451","display_name":"Logic gate","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.17686","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17686","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.17686","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17686","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":{"Deploying":[0],"event-camera":[1],"object":[2],"detectors":[3,151],"is":[4],"constrained":[5],"by":[6,85,108,166],"per-frame":[7],"labeling":[8],"requirements":[9],"and":[10,26,66,111,162],"GPU":[11,37],"compute":[12],"demands.":[13],"This":[14],"work":[15],"introduces":[16],"three":[17,48],"local":[18,163],"spike-timing-dependent":[19],"plasticity":[20],"(STDP)":[21],"modules,":[22,28],"including":[23],"sequence,":[24],"candidate,":[25],"tube-reliability":[27],"that":[29],"operate":[30],"on":[31],"a":[32,98,116,134],"single":[33],"CPU":[34],"thread":[35],"without":[36],"support.":[38],"On":[39,130],"the":[40,44,79,102,131],"FRED":[41],"drone":[42],"benchmark,":[43,133],"proposed":[45],"framework":[46],"spans":[47],"label-efficient":[49],"supervision":[50],"tiers.":[51],"A":[52],"strict":[53],"zero-label":[54],"detector":[55],"achieves":[56,71],"53.8%":[57],"mAP@30,":[58,65],"approximately":[59],"26":[60],"train-derived":[61],"bits":[62],"achieve":[63],"76.9%":[64],"an":[67],"STDP":[68,104,136],"candidate-reliability":[69],"gate":[70,81,114,121],"78.60":[72],"+/-":[73,87],"0.42%":[74],"mAP@30.":[75],"Under":[76],"acquisition-order":[77],"drift,":[78],"cohort":[80],"outperforms":[82],"streaming":[83],"k-means":[84],"2.03":[86],"0.58":[88],"percentage":[89],"points":[90],"across":[91],"20":[92,94],"of":[93,156],"positive":[95],"trials,":[96],"while":[97],"no-drift":[99],"control":[100],"falsifies":[101],"effect.":[103],"reduces":[105,138],"single-model":[106],"variance":[107],"6.6":[109],"times,":[110],"one":[112],"trained":[113],"matches":[115],"44-seed":[117],"ensemble":[118],"bound.":[119],"The":[120],"transfers":[122],"to":[123,143],"Intel":[124],"Lava":[125],"with":[126],"89%":[127],"top-2":[128],"agreement.":[129],"EVUAV":[132],"tube-level":[135],"layer":[137],"false":[139],"alarms":[140],"from":[141],"454":[142],"331e-4":[144],"at":[145],"Pd":[146],"&gt;=":[147],"88%.":[148],"Dense":[149],"gradient-trained":[150],"cannot":[152],"provide":[153],"this":[154],"combination":[155],"gradient":[157],"training,":[158],"dense":[159],"matrix":[160],"multiplication,":[161],"plasticity-free":[164],"operation":[165],"construction.":[167]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-20T00:00:00"}
