{"id":"https://openalex.org/W4417327303","doi":"https://doi.org/10.48550/arxiv.2510.21588","title":"Contribution of task-irrelevant stimuli to drift of neural representations","display_name":"Contribution of task-irrelevant stimuli to drift of neural representations","publication_year":2025,"publication_date":"2025-10-24","ids":{"openalex":"https://openalex.org/W4417327303","doi":"https://doi.org/10.48550/arxiv.2510.21588"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2510.21588","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2510.21588","pdf_url":"https://arxiv.org/pdf/2510.21588","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.21588","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026050220","display_name":"Farhad Pashakhanloo","orcid":"https://orcid.org/0000-0002-6616-2948"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Pashakhanloo, Farhad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5026050220"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10581","display_name":"Neural dynamics and brain function","score":0.2903999984264374,"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"}},"topics":[{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.2903999984264374,"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"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.16210000216960907,"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/T10427","display_name":"Visual perception and processing mechanisms","score":0.08089999854564667,"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/concept-drift","display_name":"Concept drift","score":0.5870000123977661},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5486000180244446},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5133000016212463},{"id":"https://openalex.org/keywords/models-of-neural-computation","display_name":"Models of neural computation","score":0.4909999966621399},{"id":"https://openalex.org/keywords/learning-rule","display_name":"Learning rule","score":0.47029998898506165},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4551999866962433},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.44519999623298645},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4413999915122986},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.41940000653266907},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.38670000433921814}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.633400022983551},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.5870000123977661},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5781999826431274},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5486000180244446},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5133000016212463},{"id":"https://openalex.org/C3832189","wikidata":"https://www.wikidata.org/wiki/Q8588916","display_name":"Models of neural computation","level":3,"score":0.4909999966621399},{"id":"https://openalex.org/C2779127903","wikidata":"https://www.wikidata.org/wiki/Q6510194","display_name":"Learning rule","level":3,"score":0.47029998898506165},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4551999866962433},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.44519999623298645},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4413999915122986},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.41940000653266907},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.38670000433921814},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.38449999690055847},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3776000142097473},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.3659000098705292},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3490999937057495},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3433000147342682},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.3391000032424927},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C120822770","wikidata":"https://www.wikidata.org/wiki/Q5156355","display_name":"Competitive learning","level":3,"score":0.32330000400543213},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32280001044273376},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.289000004529953},{"id":"https://openalex.org/C50335755","wikidata":"https://www.wikidata.org/wiki/Q483247","display_name":"Phenomenon","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C48164120","wikidata":"https://www.wikidata.org/wiki/Q4491893","display_name":"Concept learning","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.21588","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2510.21588","pdf_url":"https://arxiv.org/pdf/2510.21588","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.21588","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.21588","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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.21588","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2510.21588","pdf_url":"https://arxiv.org/pdf/2510.21588","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309622","display_name":"Harvard University","ror":"https://ror.org/03vek6s52"},{"id":"https://openalex.org/F4320338027","display_name":"FAS Division of Science, Harvard University","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4417327303.pdf","grobid_xml":"https://content.openalex.org/works/W4417327303.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Biological":[0],"and":[1,12,17,61,85,88,110,143,156,160,168,184,206,226,232],"artificial":[2],"learners":[3],"are":[4,93],"inherently":[5],"exposed":[6],"to":[7,66,91,125,166,229],"a":[8,49,78,105,128,169,241],"stream":[9],"of":[10,59,81,107,138,187,208,223],"data":[11,60,108,189],"experience":[13],"throughout":[14],"their":[15],"lifetimes":[16],"must":[18],"constantly":[19],"adapt":[20],"to,":[21],"learn":[22],"from,":[23],"or":[24],"selectively":[25],"ignore":[26,126],"the":[27,36,40,56,89,114,122,136,177,182,185,188,191,204,221,235,248],"ongoing":[28],"input.":[29],"Recent":[30],"findings":[31],"reveal":[32],"that,":[33],"even":[34],"when":[35],"performance":[37],"remains":[38],"stable,":[39],"underlying":[41,245],"neural":[42,75],"representations":[43],"can":[44,131],"change":[45],"gradually":[46],"over":[47],"time,":[48],"phenomenon":[50,148],"known":[51],"as":[52,104,240],"representational":[53,230],"drift.":[54],"Studying":[55],"different":[57,200],"sources":[58],"noise":[62,116],"that":[63,113,176,197],"may":[64],"contribute":[65],"drift":[67,82,103,134,178,209,231,239],"is":[68],"essential":[69],"for":[70,203,237,243],"understanding":[71],"lifelong":[72],"learning":[73,86,99,115,152,227],"in":[74,96,127,135,150,161,190,247],"systems.":[76],"However,":[77],"systematic":[79],"study":[80,219],"across":[83],"architectures":[84],"rules,":[87],"connection":[90],"task,":[92,225],"missing.":[94],"Here,":[95],"an":[97],"online":[98],"setup,":[100],"we":[101,145],"characterize":[102],"function":[106],"distribution,":[109],"specifically":[111],"show":[112,196],"induced":[117],"by":[118],"task-irrelevant":[119,192],"stimuli,":[120,224],"which":[121],"agent":[123],"learns":[124],"given":[129],"context,":[130],"create":[132],"long-term":[133],"representation":[137],"task-relevant":[139],"stimuli.":[140],"Using":[141],"theory":[142],"simulations,":[144],"demonstrate":[146],"this":[147,198],"both":[149],"Hebbian-based":[151],"--":[153,159],"Oja's":[154],"rule":[155,228],"Similarity":[157],"Matching":[158],"stochastic":[162],"gradient":[163],"descent":[164],"applied":[165],"autoencoders":[167],"supervised":[170],"two-layer":[171],"network.":[172],"We":[173,194],"consistently":[174],"observe":[175],"rate":[179],"increases":[180],"with":[181],"variance":[183],"dimension":[186],"subspace.":[193],"further":[195],"yields":[199],"qualitative":[201],"predictions":[202],"geometry":[205],"dimension-dependency":[207],"than":[210],"those":[211],"arising":[212],"from":[213],"Gaussian":[214],"synaptic":[215],"noise.":[216],"Overall,":[217],"our":[218],"links":[220],"structure":[222],"could":[233],"pave":[234],"way":[236],"using":[238],"signal":[242],"uncovering":[244],"computation":[246],"brain.":[249]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-28T00:00:00"}