{"id":"https://openalex.org/W4416149667","doi":"https://doi.org/10.52202/085713-0823","title":"Spectral Analysis of Representational Similarity with Limited Neurons","display_name":"Spectral Analysis of Representational Similarity with Limited Neurons","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416149667","doi":"https://doi.org/10.52202/085713-0823"},"language":"en","primary_location":{"id":"doi:10.52202/085713-0823","doi":"https://doi.org/10.52202/085713-0823","is_oa":false,"landing_page_url":"https://doi.org/10.52202/085713-0823","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Neural Information Processing Systems 38","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2502.19648","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102152465","display_name":"Hyunmo Kang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]},{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["KR","US"],"is_corresponding":false,"raw_author_name":"Hyunmo Kang","raw_affiliation_strings":["Johns Hopkins University, Seoul National University, Flatiron Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johns Hopkins University, Seoul National University, Flatiron Institute","institution_ids":["https://openalex.org/I139264467","https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040593057","display_name":"Abd\u00fclkadir Canatar","orcid":"https://orcid.org/0000-0002-0140-5718","observed_orcids":["https://orcid.org/0000-0002-0140-5718"]},"institutions":[{"id":"https://openalex.org/I4387153999","display_name":"Flatiron Institute","ror":"https://ror.org/00sekdz59","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210107338","https://openalex.org/I4387153999"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abdulkadir Canatar","raw_affiliation_strings":["Flatiron Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Flatiron Institute","institution_ids":["https://openalex.org/I4387153999"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016533438","display_name":"SueYeon Chung","orcid":"https://orcid.org/0000-0001-5726-4865","observed_orcids":["https://orcid.org/0000-0001-5726-4865"]},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"SueYeon Chung","raw_affiliation_strings":["Harvard University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harvard University","institution_ids":["https://openalex.org/I136199984"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"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":0.30058778,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"27617","last_page":"27651"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.3837999999523163,"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/T10320","display_name":"Neural Networks and Applications","score":0.3837999999523163,"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/T10581","display_name":"Neural dynamics and brain function","score":0.3422999978065491,"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/T11716","display_name":"Random Matrices and Applications","score":0.025499999523162842,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/representational-similarity","display_name":"representational similarity","score":0.9800000190734863},{"id":"https://openalex.org/keywords/random-matrix-theory","display_name":"random matrix theory","score":0.7160000205039978},{"id":"https://openalex.org/keywords/centered-kernel-alignment","display_name":"centered kernel alignment","score":0.8809999823570251},{"id":"https://openalex.org/keywords/canonical-correlation-analysis","display_name":"canonical correlation analysis","score":1.0},{"id":"https://openalex.org/keywords/eigenvector-delocalization","display_name":"eigenvector delocalization","score":0.9950000047683716},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"noise reduction","score":0.5839999914169312},{"id":"https://openalex.org/keywords/neural-representation","display_name":"neural representations","score":0.5820000171661377},{"id":"https://openalex.org/keywords/computational-model","display_name":"computational model","score":0.4399999976158142}],"concepts":[{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6365000009536743},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.604200005531311},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6026999950408936},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5541999936103821},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4627000093460083},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.45320001244544983},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4431999921798706},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4388999938964844},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42089998722076416},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.40630000829696655},{"id":"https://openalex.org/C89246107","wikidata":"https://www.wikidata.org/wiki/Q1398821","display_name":"Rule of thumb","level":2,"score":0.4058000147342682},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.4050999879837036},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3905999958515167},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3822999894618988},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.3671000003814697},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.3621000051498413},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3450999855995178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33410000801086426},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3018999993801117},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.30140000581741333},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3005000054836273},{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C2983668108","wikidata":"https://www.wikidata.org/wiki/Q280453","display_name":"Spectral analysis","level":3,"score":0.28290000557899475},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2720000147819519},{"id":"https://openalex.org/C64812099","wikidata":"https://www.wikidata.org/wiki/Q176604","display_name":"Random matrix","level":3,"score":0.2644999921321869},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C162392398","wikidata":"https://www.wikidata.org/wiki/Q272404","display_name":"Finite set","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.52202/085713-0823","doi":"https://doi.org/10.52202/085713-0823","is_oa":false,"landing_page_url":"https://doi.org/10.52202/085713-0823","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Neural Information Processing Systems 38","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2502.19648","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.19648","pdf_url":"https://arxiv.org/pdf/2502.19648","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2502.19648","doi":"https://doi.org/10.48550/arxiv.2502.19648","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2502.19648","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2502.19648","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.19648","pdf_url":"https://arxiv.org/pdf/2502.19648","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306164","display_name":"Simons Foundation","ror":"https://ror.org/01cmst727"},{"id":"https://openalex.org/F4320319356","display_name":"Flatiron Health","ror":"https://ror.org/0508h6p74"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416149667.pdf","grobid_xml":"https://content.openalex.org/works/W4416149667.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4416507587","https://openalex.org/W2949434404","https://openalex.org/W6929840215","https://openalex.org/W2900426157","https://openalex.org/W2511141460","https://openalex.org/W2160654481","https://openalex.org/W7097996925","https://openalex.org/W2751454695","https://openalex.org/W2942810103","https://openalex.org/W4414449749"],"abstract_inverted_index":{"Understanding":[0],"representational":[1,68],"similarity":[2,49,69],"between":[3],"neural":[4,86,167],"recordings":[5],"and":[6,57,159],"computational":[7],"models":[8],"is":[9],"essential":[10],"for":[11,67,101,130,165],"neuroscience,":[12],"yet":[13],"remains":[14],"challenging":[15],"to":[16,20,43,75,97,141],"measure":[17],"reliably":[18],"due":[19,96],"the":[21,24,76,80,108,115,119],"constraints":[22],"on":[23,52,157],"number":[25,109,120],"of":[26,79,110,118,121,128],"neurons":[27],"that":[28,71,85,107],"can":[29],"be":[30],"recorded":[31,122],"simultaneously.":[32],"In":[33],"this":[34],"work,":[35],"we":[36,105,136],"apply":[37],"tools":[38],"from":[39],"Random":[40],"Matrix":[41],"Theory":[42],"investigate":[44],"how":[45],"such":[46],"limitations":[47],"affect":[48],"measures,":[50],"focusing":[51],"Centered":[53],"Kernel":[54],"Alignment":[55],"(CKA)":[56],"Canonical":[58],"Correlation":[59],"Analysis":[60],"(CCA).":[61],"We":[62,83],"propose":[63],"an":[64],"analytical":[65],"framework":[66],"analysis":[70,147],"relates":[72],"measured":[73],"similarities":[74,87],"spectral":[77],"properties":[78],"underlying":[81],"representations.":[82],"demonstrate":[84],"are":[88,155],"systematically":[89],"underestimated":[90],"under":[91,169],"finite":[92,170],"neuron":[93,151],"sampling,":[94],"mainly":[95],"eigenvector":[98],"delocalization.":[99],"Moreover,":[100],"power-law":[102],"population":[103],"spectra,":[104],"show":[106],"localized":[111],"eigenvectors":[112],"scales":[113],"as":[114],"square":[116],"root":[117],"neurons,":[123],"providing":[124],"a":[125,138],"simple":[126],"rule":[127],"thumb":[129],"practitioners.":[131],"To":[132],"overcome":[133],"sampling":[134,171],"bias,":[135],"introduce":[137],"denoising":[139],"method":[140],"infer":[142],"population-level":[143],"similarity,":[144],"enabling":[145],"accurate":[146],"even":[148],"with":[149],"small":[150],"samples.":[152],"Theoretical":[153],"predictions":[154],"validated":[156],"synthetic":[158],"real":[160],"datasets,":[161],"offering":[162],"practical":[163],"strategies":[164],"interpreting":[166],"data":[168],"constraints.":[172]},"counts_by_year":[],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2025-10-10T00:00:00"}