{"id":"https://openalex.org/W7133357827","doi":"https://doi.org/10.48550/arxiv.2603.00588","title":"Energy-Efficient Information Representation in MNIST Classification Using Biologically Inspired Learning","display_name":"Energy-Efficient Information Representation in MNIST Classification Using Biologically Inspired Learning","publication_year":2026,"publication_date":"2026-02-28","ids":{"openalex":"https://openalex.org/W7133357827","doi":"https://doi.org/10.48550/arxiv.2603.00588"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.00588","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00588","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.2603.00588","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5107079744","display_name":"Patrick Stricker","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stricker, 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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.3675000071525574,"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":0.3675000071525574,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.1891999989748001,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.08309999853372574,"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/mnist-database","display_name":"MNIST database","score":0.8725000023841858},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.6538000106811523},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6054999828338623},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.6017000079154968},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.564300000667572},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.548799991607666},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.37860000133514404}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8725000023841858},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7307999730110168},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6542999744415283},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6538000106811523},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6054999828338623},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.6017000079154968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.573199987411499},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.564300000667572},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.548799991607666},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.3675000071525574},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C2779127903","wikidata":"https://www.wikidata.org/wiki/Q6510194","display_name":"Learning rule","level":3,"score":0.2872999906539917},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.00588","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00588","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.2603.00588","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00588","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":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.7009594440460205}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Efficient":[0],"representation":[1],"learning":[2,71],"is":[3,13],"essential":[4,104],"for":[5,44,125,140,155],"optimal":[6],"information":[7],"storage":[8,118],"and":[9,38,49,61,100,117,132,147,150,163],"classification.":[10],"However,":[11],"it":[12,109],"frequently":[14],"overlooked":[15],"in":[16,24,113],"artificial":[17],"neural":[18],"networks":[19,25],"(ANNs).":[20],"This":[21,143],"neglect":[22],"results":[23],"that":[26,159],"can":[27],"become":[28],"overparameterized":[29],"by":[30,96],"factors":[31],"of":[32,106,115,127],"up":[33],"to":[34,137],"13,":[35],"increasing":[36],"redundancy":[37],"energy":[39],"consumption.":[40],"As":[41],"the":[42,80,89,103,123,134],"demand":[43],"large":[45],"language":[46],"models":[47,158],"(LLMs)":[48],"their":[50],"scale":[51],"increase,":[52],"these":[53],"issues":[54],"are":[55],"further":[56],"highlighted,":[57],"raising":[58],"significant":[59],"ethical":[60],"environmental":[62],"concerns.":[63],"We":[64],"analyze":[65],"our":[66],"previously":[67],"developed":[68],"biologically":[69],"inspired":[70],"rule":[72],"using":[73],"information-theoretic":[74],"concepts,":[75],"evaluating":[76],"its":[77],"efficiency":[78,116],"on":[79],"MNIST":[81],"classification":[82],"task.":[83],"The":[84],"proposed":[85],"rule,":[86],"which":[87],"emulates":[88],"brain's":[90,135],"structural":[91],"plasticity,":[92],"naturally":[93],"prevents":[94],"overparameterization":[95],"optimizing":[97],"synaptic":[98],"usage":[99],"retaining":[101],"only":[102],"number":[105],"synapses.":[107],"Furthermore,":[108],"outperforms":[110],"backpropagation":[111],"(BP)":[112],"terms":[114],"capacity.":[119],"It":[120],"also":[121],"eliminates":[122],"need":[124],"pre-optimization":[126],"network":[128],"architecture,":[129],"enhances":[130],"adaptability,":[131],"reflects":[133],"ability":[136],"reserve":[138],"'space'":[139],"new":[141],"memories.":[142],"approach":[144],"advances":[145],"scalable":[146],"energy-efficient":[148],"AI":[149],"provides":[151],"a":[152],"promising":[153],"framework":[154],"developing":[156],"brain-inspired":[157],"optimize":[160],"resource":[161],"allocation":[162],"adaptability.":[164]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-04T00:00:00"}