{"id":"https://openalex.org/W7133481554","doi":"https://doi.org/10.48550/arxiv.2603.03234","title":"Guiding Sparse Neural Networks with Neurobiological Principles to Elicit Biologically Plausible Representations","display_name":"Guiding Sparse Neural Networks with Neurobiological Principles to Elicit Biologically Plausible Representations","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7133481554","doi":"https://doi.org/10.48550/arxiv.2603.03234"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.03234","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03234","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.03234","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128119426","display_name":"Patrick Inoue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Inoue, Patrick","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047626282","display_name":"Florian R\u00f6hrbein","orcid":"https://orcid.org/0000-0002-4709-2673"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"R\u00f6hrbein, Florian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5032109856","display_name":"Andreas Knoblauch","orcid":"https://orcid.org/0000-0002-2534-0250"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Knoblauch, Andreas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9266999959945679,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9266999959945679,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.010599999688565731,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.005900000222027302,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.6209999918937683},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5439000129699707},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.40049999952316284},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.375},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.367900013923645},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.352400004863739}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6832000017166138},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6808000206947327},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6209999918937683},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5439000129699707},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4756999909877777},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.40049999952316284},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.375},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.352400004863739},{"id":"https://openalex.org/C2984127161","wikidata":"https://www.wikidata.org/wiki/Q969316","display_name":"Neural activity","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C2986949344","wikidata":"https://www.wikidata.org/wiki/Q9404","display_name":"Neural system","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C28225019","wikidata":"https://www.wikidata.org/wiki/Q4915005","display_name":"Biological network","level":2,"score":0.2646999955177307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.03234","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03234","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":"doi:10.48550/arxiv.2603.03234","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03234","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"deep":[1],"neural":[2,32,64,128,138,158],"networks":[3],"(DNNs)":[4],"have":[5],"achieved":[6],"remarkable":[7],"performance":[8],"in":[9,30,63,113],"tasks":[10],"such":[11],"as":[12],"image":[13],"recognition,":[14],"they":[15],"often":[16],"struggle":[17],"with":[18,96],"generalization,":[19,111],"learning":[20,46,73,115],"from":[21,149],"few":[22],"examples,":[23],"and":[24,85,108],"continuous":[25],"adaptation":[26],"-":[27],"abilities":[28],"inherent":[29],"biological":[31,49],"systems.":[33],"These":[34],"challenges":[35],"arise":[36],"due":[37],"to":[38,41,87,122,151],"DNNs'":[39],"failure":[40],"emulate":[42],"the":[43,57,123,131],"efficient,":[44],"adaptive":[45],"mechanisms":[47],"of":[48,59,125,133],"networks.":[50],"To":[51],"address":[52],"these":[53,97,119],"issues,":[54],"we":[55],"explore":[56],"integration":[58],"neurobiologically":[60],"inspired":[61,72],"assumptions":[62,136],"network":[65,139],"learning.":[66],"This":[67],"study":[68],"introduces":[69],"a":[70],"biologically":[71,126],"rule":[74],"that":[75,144],"naturally":[76],"integrates":[77],"neurobiological":[78,99,135],"principles,":[79,100],"including":[80],"sparsity,":[81],"lognormal":[82],"weight":[83],"distributions,":[84],"adherence":[86],"Dale's":[88],"law,":[89],"without":[90],"requiring":[91],"explicit":[92],"enforcement.":[93],"By":[94],"aligning":[95],"core":[98],"our":[101],"model":[102],"enhances":[103],"robustness":[104],"against":[105],"adversarial":[106],"attacks":[107],"demonstrates":[109],"superior":[110],"particularly":[112],"few-shot":[114],"scenarios.":[116],"Notably,":[117],"integrating":[118],"constraints":[120],"leads":[121],"emergence":[124],"plausible":[127],"representations,":[129],"underscoring":[130],"efficacy":[132],"incorporating":[134],"into":[137,157],"design.":[140],"Preliminary":[141],"results":[142],"suggest":[143],"this":[145],"approach":[146],"could":[147],"extend":[148],"feature-specific":[150],"task-specific":[152],"encoding,":[153],"potentially":[154],"offering":[155],"insights":[156],"resource":[159],"allocation":[160],"for":[161],"complex":[162],"tasks.":[163]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-05T00:00:00"}
