{"id":"https://openalex.org/W7154580478","doi":"https://doi.org/10.14428/esann/2026.es2026-168","title":"Predictive Coding inspired convolutional networks can capture the neural dynamics of recurrent processing in human image recognition","display_name":"Predictive Coding inspired convolutional networks can capture the neural dynamics of recurrent processing in human image recognition","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7154580478","doi":"https://doi.org/10.14428/esann/2026.es2026-168"},"language":null,"primary_location":{"id":"doi:10.14428/esann/2026.es2026-168","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2026.es2026-168","pdf_url":"https://doi.org/10.14428/esann/2026.es2026-168","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2026 proceesdings","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.14428/esann/2026.es2026-168","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133798067","display_name":"Manshan Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manshan Guo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069173567","display_name":"Bhavin Choksi","orcid":"https://orcid.org/0000-0002-6475-4149"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bhavin Choksi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133777712","display_name":"sari sadiya","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"sari sadiya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003186916","display_name":"Pablo Oyarzo","orcid":"https://orcid.org/0000-0002-2706-5889"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pablo Oyarzo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125532305","display_name":"Radoslaw Cichy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Radoslaw Cichy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102863049","display_name":"Gemma Roig","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gemma Roig","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.52268658,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"493","last_page":"498"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.1421000063419342,"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":0.1421000063419342,"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":0.09650000184774399,"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"}},{"id":"https://openalex.org/T11094","display_name":"Face Recognition and Perception","score":0.057100001722574234,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5755000114440918},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5121999979019165},{"id":"https://openalex.org/keywords/predictive-coding","display_name":"Predictive coding","score":0.48840001225471497},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43700000643730164},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.40310001373291016},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3808000087738037},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32499998807907104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7103000283241272},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6930999755859375},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5755000114440918},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5121999979019165},{"id":"https://openalex.org/C2778061373","wikidata":"https://www.wikidata.org/wiki/Q1315146","display_name":"Predictive coding","level":3,"score":0.48840001225471497},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43700000643730164},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42719998955726624},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3808000087738037},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.27399998903274536},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2653999924659729},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14428/esann/2026.es2026-168","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2026.es2026-168","pdf_url":"https://doi.org/10.14428/esann/2026.es2026-168","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2026 proceesdings","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.14428/esann/2026.es2026-168","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2026.es2026-168","pdf_url":"https://doi.org/10.14428/esann/2026.es2026-168","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2026 proceesdings","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G429533842","display_name":null,"funder_award_id":"CI241/3-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6058657651","display_name":null,"funder_award_id":"CI241/7-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6440785352","display_name":null,"funder_award_id":"CI241/1-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G8467423616","display_name":null,"funder_award_id":"FOR 5368","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320324521","display_name":"Freie Universit\u00e4t Berlin","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7154580478.pdf","grobid_xml":"https://content.openalex.org/works/W7154580478.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Inspired":[0],"by":[1],"the":[2,90,111,133,138,145],"robustness":[3],"of":[4,61,95,99,110,147,153],"human":[5,114,154],"vision,":[6,155],"various":[7],"attempts":[8],"have":[9],"been":[10,23,46],"made":[11],"to":[12,39,48,73,84,128],"incorporate":[13],"brain-inspired":[14,80],"mechanisms":[15],"into":[16],"artificial":[17],"neural":[18,86],"networks.A":[19],"popular":[20,101],"candidate":[21],"has":[22,45],"predictive":[24,97],"coding,":[25],"a":[26,58,96,100,107],"prominent":[27],"theory":[28],"in":[29,43,150],"neuroscience,":[30],"that":[31,33,120,132],"theorizes":[32],"feedback":[34],"connections":[35],"communicate":[36],"top-down":[37],"predictions":[38],"earlier":[40],"regions.While":[41],"recurrence":[42],"models":[44,152],"demonstrated":[47],"be":[49,74],"useful":[50],"when":[51,117],"processing":[52],"noisy":[53],"and":[54],"di":[55,126],"cult":[56,127],"stimuli,":[57],"direct":[59],"evidence":[60],"its":[62],"utility":[63],"for":[64,160],"explaining":[65],"brain":[66,91],"data":[67],"under":[68],"such":[69,79],"situation":[70],"was":[71],"yet":[72],"shown.Here,":[75],"we":[76,88],"investigated":[77],"whether":[78],"mechanism":[81],"actually":[82],"helps":[83],"capture":[85],"dynamics.Specifically,":[87],"measured":[89],"alignment":[92,140],"between":[93],"representations":[94],"version":[98],"feedforward":[102],"CNN":[103],"often":[104],"used":[105],"as":[106],"computational":[108,151],"model":[109],"visual":[112],"cortex-VGG16-and":[113],"EEG":[115,142],"collected":[116],"viewing":[118],"images":[119],"were":[121],"relatively":[122],"easy":[123],"(Control)":[124],"or":[125],"classify":[129],"(Challenge).We":[130],"demonstrate":[131],"recurrent":[134,148],"dynamics":[135],"significantly":[136],"enhanced":[137],"model's":[139],"with":[141],"responses,":[143],"underscoring":[144],"importance":[146],"connectivity":[149],"an":[156],"eect":[157],"distinctly":[158],"visible":[159],"challenging":[161],"stimuli.":[162]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-17T00:00:00"}