{"id":"https://openalex.org/W7154730159","doi":"https://doi.org/10.1016/j.engappai.2026.114778","title":"Probabilistic reliability modeling and soft-error tolerance for neuromorphic spiking neural networks in high-flux radiation environments","display_name":"Probabilistic reliability modeling and soft-error tolerance for neuromorphic spiking neural networks in high-flux radiation environments","publication_year":2026,"publication_date":"2026-04-17","ids":{"openalex":"https://openalex.org/W7154730159","doi":"https://doi.org/10.1016/j.engappai.2026.114778"},"language":"en","primary_location":{"id":"doi:10.1016/j.engappai.2026.114778","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.engappai.2026.114778","pdf_url":null,"source":{"id":"https://openalex.org/S900972176","display_name":"Engineering Applications of Artificial Intelligence","issn_l":"0952-1976","issn":["0952-1976","1873-6769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Applications of Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.engappai.2026.114778","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086115397","display_name":"Sylvester Kaczmarek","orcid":null},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Sylvester Kaczmarek","raw_affiliation_strings":["Department of Computing, Imperial College London, London, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-0393-228X","affiliations":[{"raw_affiliation_string":"Department of Computing, Imperial College London, London, United Kingdom","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5086115397"],"corresponding_institution_ids":["https://openalex.org/I47508984"],"apc_list":{"value":3170,"currency":"USD","value_usd":3170},"apc_paid":{"value":3170,"currency":"USD","value_usd":3170},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.75230653,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"176","issue":null,"first_page":"114778","last_page":"114778"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9193000197410583,"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":0.9193000197410583,"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/T11005","display_name":"Radiation Effects in Electronics","score":0.029999999329447746,"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":0.007699999958276749,"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/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.883899986743927},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.8144000172615051},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.635200023651123},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5665000081062317},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5224999785423279},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4586000144481659}],"concepts":[{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.883899986743927},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.8144000172615051},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7949000000953674},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.635200023651123},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5665000081062317},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5224999785423279},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5171999931335449},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4586000144481659},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.43540000915527344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3465999960899353},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.engappai.2026.114778","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.engappai.2026.114778","pdf_url":null,"source":{"id":"https://openalex.org/S900972176","display_name":"Engineering Applications of Artificial Intelligence","issn_l":"0952-1976","issn":["0952-1976","1873-6769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Applications of Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.engappai.2026.114778","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.engappai.2026.114778","pdf_url":null,"source":{"id":"https://openalex.org/S900972176","display_name":"Engineering Applications of Artificial Intelligence","issn_l":"0952-1976","issn":["0952-1976","1873-6769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Applications of Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.7960307002067566}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2006370340","https://openalex.org/W2048460092","https://openalex.org/W2141068710","https://openalex.org/W2145071552","https://openalex.org/W2148156265","https://openalex.org/W2325092268","https://openalex.org/W2783525259","https://openalex.org/W3088586141","https://openalex.org/W3127940238","https://openalex.org/W4210357113","https://openalex.org/W4386496119","https://openalex.org/W4393338007","https://openalex.org/W4415227140","https://openalex.org/W7128677654"],"related_works":[],"abstract_inverted_index":{"Neuromorphic":[0],"processors":[1],"enable":[2],"ultra-low-power":[3],"edge":[4],"inference":[5,35],"for":[6,31,187],"safety-critical":[7],"aerospace":[8],"Artificial":[9,192],"Intelligence":[10,193],"applications,":[11],"but":[12],"ionizing":[13],"radiation":[14,196],"can":[15],"induce":[16],"soft":[17],"errors":[18],"that":[19,50,95,131],"degrade":[20,136],"spiking":[21,32,133],"neural":[22,33,134],"networks.":[23],"This":[24],"paper":[25],"develops":[26],"a":[27,46,63,92,177,184],"probabilistic":[28,67],"reliability":[29,97],"model":[30],"network":[34],"under":[36,83,152],"single-event":[37],"upsets":[38],"in":[39,80,194],"synaptic":[40],"memory":[41],"and":[42,73,99,108,127,148,170],"neuronal":[43],"state,":[44],"using":[45,121],"Poisson":[47],"fault-injection":[48],"framework":[49],"maps":[51],"particle":[52,156],"flux":[53,106],"to":[54,76,102,137,174],"bit-flip":[55],"rates.":[56,158],"We":[57,89],"propose":[58],"the":[59,122,143,161],"Hierarchical":[60,162],"Temporal":[61,163],"Defense,":[62],"bio-inspired":[64],"architecture":[65],"combining":[66],"input":[68],"encoding,":[69],"homeostatic":[70],"threshold":[71],"adaptation,":[72],"volatility-gated":[74],"plasticity":[75],"suppress":[77],"error":[78],"propagation":[79],"temporal":[81],"codes":[82],"high":[84],"linear":[85],"energy":[86],"transfer":[87],"conditions.":[88],"also":[90],"provide":[91],"certification-oriented":[93],"methodology":[94],"derives":[96],"curves":[98],"mean":[100,145,172],"time":[101,173],"failure":[103,112,175],"from":[104],"measurable":[105],"conditions":[107],"an":[109,117,138],"explicit":[110],"functional":[111],"threshold.":[113],"Hardware-in-the-loop":[114],"experiments":[115],"on":[116,142],"Akida":[118],"neuromorphic":[119],"processor,":[120],"publicly":[123],"available":[124],"Cislunar":[125],"Anomaly":[126],"Risk":[128],"Dataset,":[129],"show":[130],"baseline":[132],"networks":[135],"anomaly-detection":[139],"score":[140,167],"based":[141],"harmonic":[144],"of":[146,179],"precision":[147],"recall":[149],"below":[150],"0.48":[151],"severe":[153],"solar":[154],"energetic":[155],"event":[157],"In":[159],"contrast,":[160],"Defense":[164],"maintains":[165],"this":[166],"above":[168],"0.72":[169],"increases":[171],"by":[176],"factor":[178],"4.5.":[180],"These":[181],"results":[182],"demonstrate":[183],"viable":[185],"pathway":[186],"deploying":[188],"highly":[189],"efficient,":[190],"fault-tolerant":[191],"extreme":[195],"environments.":[197]},"counts_by_year":[],"updated_date":"2026-05-07T06:04:25.777469","created_date":"2026-04-18T00:00:00"}