{"id":"https://openalex.org/W4386349971","doi":"https://doi.org/10.3389/fncom.2023.1276998","title":"Editorial: Deep neural network based decision-making interpretability","display_name":"Editorial: Deep neural network based decision-making interpretability","publication_year":2023,"publication_date":"2023-09-01","ids":{"openalex":"https://openalex.org/W4386349971","doi":"https://doi.org/10.3389/fncom.2023.1276998","pmid":"https://pubmed.ncbi.nlm.nih.gov/37727153"},"language":"en","primary_location":{"id":"doi:10.3389/fncom.2023.1276998","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fncom.2023.1276998","pdf_url":"https://public-pages-files-2025.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2023.1276998/pdf","source":{"id":"https://openalex.org/S19778766","display_name":"Frontiers in Computational Neuroscience","issn_l":"1662-5188","issn":["1662-5188"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computational Neuroscience","raw_type":"journal-article"},"type":"editorial","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://public-pages-files-2025.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2023.1276998/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027627793","display_name":"Guitao Cao","orcid":"https://orcid.org/0000-0002-4059-4806"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guitao Cao","raw_affiliation_strings":["Software Engineering Institute, East China Normal University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Software Engineering Institute, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101430356","display_name":"Ye Duan","orcid":"https://orcid.org/0000-0002-1166-7703"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ye Duan","raw_affiliation_strings":["School of Computing, Clemson University, Clemson, SC, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, Clemson University, Clemson, SC, United States","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100625024","display_name":"Wenming Cao","orcid":"https://orcid.org/0000-0002-8174-6167"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenming Cao","raw_affiliation_strings":["College of Information Engineering, Shenzhen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5027627793"],"corresponding_institution_ids":["https://openalex.org/I66867065"],"apc_list":{"value":3295,"currency":"USD","value_usd":3295},"apc_paid":{"value":3295,"currency":"USD","value_usd":3295},"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":null,"first_page":"1276998","last_page":"1276998"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.7246000170707703,"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"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.7246000170707703,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.6958000063896179,"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/interpretability","display_name":"Interpretability","score":0.9567116498947144},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6166799664497375},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6044625043869019},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5018100738525391},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4522547721862793},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.450234979391098}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9567116498947144},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6166799664497375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6044625043869019},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5018100738525391},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4522547721862793},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.450234979391098}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3389/fncom.2023.1276998","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fncom.2023.1276998","pdf_url":"https://public-pages-files-2025.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2023.1276998/pdf","source":{"id":"https://openalex.org/S19778766","display_name":"Frontiers in Computational Neuroscience","issn_l":"1662-5188","issn":["1662-5188"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computational Neuroscience","raw_type":"journal-article"},{"id":"pmid:37727153","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37727153","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":"Frontiers in computational neuroscience","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10505711","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10505711","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10505711/pdf/fncom-17-1276998.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Front Comput Neurosci","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:1461ab6b46a84c34ab3bc6bc5fb97a21","is_oa":true,"landing_page_url":"https://doaj.org/article/1461ab6b46a84c34ab3bc6bc5fb97a21","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frontiers in Computational Neuroscience, Vol 17 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/fncom.2023.1276998","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fncom.2023.1276998","pdf_url":"https://public-pages-files-2025.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2023.1276998/pdf","source":{"id":"https://openalex.org/S19778766","display_name":"Frontiers in Computational Neuroscience","issn_l":"1662-5188","issn":["1662-5188"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computational Neuroscience","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386349971.pdf","grobid_xml":"https://content.openalex.org/works/W4386349971.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4385957992","https://openalex.org/W4229079080","https://openalex.org/W4206534706","https://openalex.org/W4385965371","https://openalex.org/W4386025632","https://openalex.org/W3006943036","https://openalex.org/W4200511449","https://openalex.org/W4299487748","https://openalex.org/W4366768489","https://openalex.org/W3012234327"],"abstract_inverted_index":{"Deep":[0],"Neural":[1],"Networks":[2],"(DNNs)":[3],"have":[4,159],"emerged":[5],"as":[6,25,452],"a":[7,53,95,125,187,203,255,261,265,285,297,309,328,362,379,403,426,469,479,509,515,546,563,613,647,668,703],"powerful":[8],"tool,":[9],"capable":[10],"of":[11,21,37,72,104,124,146,171,215,223,277,288,303,335,344,365,382,419,440,465,471,539,566,569,578,592,639,671,675,690],"making":[12],"complex":[13,92],"decisions":[14,345,476],"that":[15,139,432,482,660],"were":[16],"once":[17],"the":[18,35,41,70,78,85,122,163,169,174,177,213,221,269,275,289,320,325,342,348,374,408,416,438,458,474,484,490,532,537,540,567,576,587,602,609,632,640,676,683,688,697],"exclusive":[19],"domain":[20],"human":[22,570],"cognition.":[23],"However,":[24],"these":[26,336,370,420],"systems":[27],"become":[28,52],"increasingly":[29],"integrated":[30],"into":[31,59,112,245,388,586],"our":[32],"daily":[33],"lives,":[34],"question":[36],"their":[38,47,278],"interpretability":[39,82,103,114,119,145,161,240,244,417,472,538,606,620],"\u2014":[40,50],"ability":[42,123],"to":[43,77,84,88,121,127,173,231,259,267,283,323,340,351,406,456,473,504,518,601,622,630,651,662],"understand":[44,407],"and":[45,62,68,115,198,202,219,254,308,372,397,487,531,557,590,597,636,655,693],"explain":[46,341,631],"decision-making":[48,178,257,262,291,542,603],"processes":[49],"has":[51],"pressing":[54],"concern.":[55],"This":[56,281,400,445,512,582,665],"editorial":[57],"delves":[58],"this":[60,75,700],"issue,":[61],"based":[63],"on":[64,211,358,575,696],"existing":[65],"findings,":[66],"analyzes":[67],"summarizes":[69],"contributions":[71],"articles":[73],"within":[74],"topic":[76],"research":[79,235,418],"objective.":[80],"Model":[81],"refers":[83,120],"model\u2019s":[86,238,290,475,541,698],"capacity":[87],"elucidate":[89],"or":[90,136,626],"present":[91,495],"concepts":[93],"in":[94,130,319,327,489,508,525],"human-readable":[96],"manner,":[97],"enabling":[98],"comprehension":[99],"by":[100,132,153,241,347,477,544],"individuals.":[101],"The":[102,143,271,312,332,376,463],"machine":[105],"learning":[106,617],"models":[107,138,149,629],"can":[108,150,338],"be":[109,128,151],"broadly":[110],"categorized":[111],"ante-hoc":[113],"post-hoc":[116,619],"interpretability.":[117,353,604],"Ante-hoc":[118],"model":[126,175,247,610,681],"interpretable":[129],"itself":[131],"training":[133,300,658,692],"simple":[134],"structures":[135,486],"self-explanatory":[137],"are":[140,317,555],"inherently":[141],"interpretable.":[142],"inherent":[144,239,485],"neural":[147,595,678],"network":[148,431,502,679],"achieved":[152],"introducing":[154,227],"attention":[155,164,181,206,229],"mechanisms.":[156],"Attention":[157],"mechanisms":[158],"good":[160],"since":[162],"weight":[165],"matrix":[166],"directly":[167,242],"reflects":[168],"areas":[170],"interest":[172],"during":[176],"process.":[179,292],"With":[180],"mechanism,":[182],"Geng":[183],"et":[184,250,294,355,423,493,560,644],"al.":[185,295,356,424,494,561,645],"propose":[186,296,378,646],"novel":[188,298,427,446],"network,":[189],"SFLCA-Net,":[190],"for":[191,412,437,520,550],"tic":[192,217],"recognition.":[193],"SFLCA-Net":[194],"uses":[195,514],"two":[196],"fast":[197],"slow":[199],"branch":[200],"subnetworks":[201],"light-efficient":[204],"channel":[205,225],"(LCA)":[207],"module.":[208],"It":[209,535],"focuses":[210,357],"capturing":[212],"characteristics":[214],"fine":[216],"movements":[218],"improving":[220],"complementarity":[222],"spatial-temporal":[224],"information,":[226],"an":[228,304,496],"module":[230],"enhance":[232],"decision-making.":[233],"Existing":[234],"also":[236],"achieve":[237],"incorporating":[243],"specific":[246],"structures.":[248],"Chen":[249],"al.utilize":[251],"multi-label":[252],"classification":[253,367,384],"dual-component":[256],"process":[258,543],"build":[260],"model,":[263,349,618],"presenting":[264],"way":[266,405],"interpret":[268],"categorization.":[270],"authors":[272,313,377],"theoretically":[273],"verify":[274],"efficiency":[276],"control":[279],"strategy.":[280],"helps":[282],"establish":[284],"transparent":[286],"understanding":[287],"He":[293,492],"two-stage":[299],"method":[301],"consisting":[302],"initial":[305],"prediction":[306],"stage":[307,322],"fine-tuning":[310,321],"stage.":[311],"introduce":[314,425],"CT-GCL,":[315],"which":[316,448],"used":[318],"reconstruct":[324],"sequence":[326,343],"causal,":[329],"temporal":[330],"order.":[331],"causal":[333],"nature":[334],"layers":[337,674],"help":[339],"made":[346],"contributing":[350],"its":[352],"Zhang":[354],"few-shot":[359,366,383,413,510],"learning,":[360,414],"providing":[361,545],"detailed":[363,380],"taxonomy":[364,381,401],"methods,":[368,371,385,394,396],"comparing":[369],"discussing":[373],"scenarios.":[375],"classifying":[386],"them":[387],"four":[389],"categories:":[390],"data":[391,451,695],"augmentation,":[392],"metric-based":[393],"optimization":[395],"model-based":[398],"methods.":[399],"provides":[402,584,702],"structured":[404],"different":[409,461,588,673],"strategies":[410],"employed":[411],"supporting":[415],"models.":[421],"Li":[422],"Long-and":[428],"Short-term":[429],"Time-series":[430],"utilizes":[433],"geometric":[434,466],"algebra":[435,467],"(GA-LSTNet)":[436],"analysis":[439,649],"multi-dimensional":[441,450],"time-series":[442],"(MTS)":[443],"data.":[444,491,581],"approach,":[447],"treats":[449],"GA":[453],"multi-vectors,":[454],"aims":[455,621],"preserve":[457],"correlation":[459],"among":[460],"dimensions.":[462],"use":[464,577],"adds":[468],"layer":[470],"offering":[478],"mathematical":[480],"framework":[481],"captures":[483],"relationships":[488],"innovative":[497],"Hough":[498,516],"matching":[499],"feature":[500],"enhancement":[501],"designed":[503],"address":[505],"object":[506,522,553],"counting":[507],"scenario.":[511],"approach":[513,650,701],"space":[517],"vote":[519],"candidate":[521,552],"regions,":[523],"resulting":[524],"reliable":[526],"similarity":[527],"maps":[528],"between":[529],"exemplars":[530],"query":[533],"image.":[534],"enhances":[536],"clear,":[547],"geometric-based":[548],"mechanism":[549],"how":[551,598,672],"regions":[554],"identified":[556],"selected.":[558],"Usman":[559],"provide":[562],"comprehensive":[564,705],"survey":[565],"field":[568],"motion":[571],"prediction,":[572],"particularly":[573],"focusing":[574],"3D":[579],"skeleton":[580],"review":[583],"insight":[585],"approaches":[589],"techniques":[591],"various":[593],"deep":[594,616],"networks,":[596],"they":[599],"contribute":[600,661],"Post-hoc":[605],"occurs":[607],"after":[608],"training.":[611],"For":[612],"given":[614],"well-trained":[615],"utilize":[623],"explanatory":[624],"methods":[625],"construct":[627],"interpretive":[628],"functioning,":[633],"decision":[634,637],"behavior,":[635],"rationales":[638],"learned":[641],"model.":[642],"Aamir":[643],"layer-wise":[648],"determine":[652],"influence":[653,682,689],"scores":[654],"identify":[656],"influential":[657],"images":[659],"class":[663],"prediction.":[664],"technique":[666],"offers":[667],"granular":[669],"view":[670],"convolutional":[677],"(CNN)":[680],"final":[684],"decision.":[685],"By":[686],"analyzing":[687],"both":[691],"testing":[694],"decisions,":[699],"more":[704],"understanding.":[706]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
