{"id":"https://openalex.org/W4388890729","doi":"https://doi.org/10.48550/arxiv.2311.10898","title":"On Functional Activations in Deep Neural Networks","display_name":"On Functional Activations in Deep Neural Networks","publication_year":2023,"publication_date":"2023-11-17","ids":{"openalex":"https://openalex.org/W4388890729","doi":"https://doi.org/10.48550/arxiv.2311.10898"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2311.10898","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.10898","pdf_url":"https://arxiv.org/pdf/2311.10898","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2311.10898","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029597528","display_name":"Andrew S. Nencka","orcid":"https://orcid.org/0000-0001-5268-2718"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nencka, Andrew S.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091206345","display_name":"L. Tugan Muftuler","orcid":"https://orcid.org/0000-0002-6635-5678"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Muftuler, L. Tugan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032574031","display_name":"Peter S. LaViolette","orcid":"https://orcid.org/0000-0002-9602-6891"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"LaViolette, Peter","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5007441935","display_name":"Kevin M. Koch","orcid":"https://orcid.org/0000-0003-4490-9761"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koch, Kevin M.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5029597528"],"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/T10028","display_name":"Topic Modeling","score":0.9474999904632568,"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/T10028","display_name":"Topic Modeling","score":0.9474999904632568,"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/computer-science","display_name":"Computer science","score":0.7187501192092896},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6705062985420227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6149236559867859},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5534647703170776},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5196551084518433},{"id":"https://openalex.org/keywords/neuroimaging","display_name":"Neuroimaging","score":0.500007152557373},{"id":"https://openalex.org/keywords/functional-neuroimaging","display_name":"Functional neuroimaging","score":0.47510087490081787},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4422798454761505},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4195787310600281},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4144488573074341},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3422728478908539},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.1651984453201294},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.09741842746734619}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7187501192092896},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6705062985420227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6149236559867859},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5534647703170776},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5196551084518433},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.500007152557373},{"id":"https://openalex.org/C52338299","wikidata":"https://www.wikidata.org/wiki/Q1004354","display_name":"Functional neuroimaging","level":3,"score":0.47510087490081787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4422798454761505},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4195787310600281},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4144488573074341},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3422728478908539},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.1651984453201294},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.09741842746734619},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2311.10898","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.10898","pdf_url":"https://arxiv.org/pdf/2311.10898","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2311.10898","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2311.10898","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2311.10898","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.10898","pdf_url":"https://arxiv.org/pdf/2311.10898","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4388890729.pdf","grobid_xml":"https://content.openalex.org/works/W4388890729.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2506266834","https://openalex.org/W2499037785","https://openalex.org/W2028174874","https://openalex.org/W2351278828","https://openalex.org/W4308957544","https://openalex.org/W2239282469","https://openalex.org/W67648935","https://openalex.org/W1993895507","https://openalex.org/W2122202779","https://openalex.org/W1995600417"],"abstract_inverted_index":{"Background:":[0],"Deep":[1],"neural":[2,211],"networks":[3,41,149,166,180,191,212,222],"have":[4,22],"proven":[5],"to":[6,55,61,76,86,121,132,135,187,194,209,216,238],"be":[7,207],"powerful":[8],"computational":[9],"tools":[10],"for":[11,226],"modeling,":[12],"prediction,":[13],"and":[14,94,162,175,181,235],"generation.":[15],"However,":[16],"the":[17,31,45,48,78,109,133,144,189,197,224],"workings":[18],"of":[19,33,42,50,69,111,172,177,203,232],"these":[20],"models":[21,35,129],"generally":[23],"been":[24],"opaque.":[25],"Recent":[26],"work":[27],"has":[28],"shown":[29,193],"that":[30],"performance":[32,171],"some":[34],"are":[36,53],"modulated":[37],"by":[38],"overlapping":[39,148],"functional":[40,51,64,179,190,204,221],"connections":[43],"within":[44],"models.":[46],"Here":[47],"techniques":[49,202],"neuroimaging":[52,205],"applied":[54,208],"an":[56,99,123],"exemplary":[57],"large":[58],"language":[59],"model":[60,229,233],"probe":[62,77,217],"its":[63],"structure.":[65],"Methods:":[66],"A":[67],"series":[68],"block-designed":[70],"task-based":[71],"prompt":[72],"sequences":[73],"were":[74,119,130,141,150,167],"generated":[75],"Facebook":[79],"Galactica-125M":[80],"model.":[81],"Tasks":[82],"included":[83],"prompts":[84,103],"relating":[85],"political":[87],"science,":[88],"medical":[89,160],"imaging,":[90],"paleontology,":[91],"archeology,":[92],"pathology,":[93],"random":[95,106],"strings":[96],"presented":[97,198],"in":[98,183,228,240],"off/on/off":[100],"pattern":[101],"with":[102,143,152],"about":[104],"other":[105],"topics.":[107],"For":[108],"generation":[110],"each":[112,153],"output":[113,117,138],"token,":[114],"all":[115],"layer":[116,137],"values":[118,139],"saved":[120],"create":[122],"effective":[124],"time":[125],"series.":[126],"General":[127],"linear":[128],"fit":[131],"data":[134],"identify":[136,196],"which":[140],"active":[142],"tasks.":[145],"Results:":[146],"Distinct,":[147],"identified":[151,178],"task.":[154,199],"Most":[155],"overlap":[156],"was":[157,192],"observed":[158],"between":[159],"imaging":[161],"pathology":[163],"networks.":[164],"These":[165],"repeatable":[168],"across":[169],"repeated":[170],"related":[173],"tasks,":[174],"correspondence":[176],"activation":[182],"tasks":[184],"not":[185],"used":[186],"define":[188],"accurately":[195],"Conclusion:":[200],"The":[201],"can":[206],"deep":[210],"as":[213],"a":[214],"means":[215],"their":[218],"workings.":[219],"Identified":[220],"hold":[223],"potential":[225],"use":[227],"alignment,":[230],"modulation":[231],"output,":[234],"identifying":[236],"weights":[237],"target":[239],"fine-tuning.":[241]},"counts_by_year":[],"updated_date":"2026-03-13T16:22:10.518609","created_date":"2025-10-10T00:00:00"}
