{"id":"https://openalex.org/W2891473114","doi":"https://doi.org/10.1109/aipr.2017.8457937","title":"The Anatomy of a Neural Network","display_name":"The Anatomy of a Neural Network","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2891473114","doi":"https://doi.org/10.1109/aipr.2017.8457937","mag":"2891473114"},"language":"en","primary_location":{"id":"doi:10.1109/aipr.2017.8457937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2017.8457937","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051920084","display_name":"James P. Larue","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"James P. LaRue","raw_affiliation_strings":["PhD, Jadco Signals"],"affiliations":[{"raw_affiliation_string":"PhD, Jadco Signals","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109247395","display_name":"Richard L. Tutwiler","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard L. Tutwiler","raw_affiliation_strings":["Professor Emeritus ARL / Penn State University/CEO LiveMotion3D LLC"],"affiliations":[{"raw_affiliation_string":"Professor Emeritus ARL / Penn State University/CEO LiveMotion3D LLC","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035838326","display_name":"Dennison J. Larue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dennison J. LaRue","raw_affiliation_strings":["Juris Doctor Candidate 2020, South Carolina School of Law"],"affiliations":[{"raw_affiliation_string":"Juris Doctor Candidate 2020, South Carolina School of Law","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5051920084"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.195,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66156332,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.982699990272522,"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":0.7033357620239258},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6694031953811646},{"id":"https://openalex.org/keywords/content-addressable-memory","display_name":"Content-addressable memory","score":0.5767549872398376},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5142955183982849},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5081596374511719},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4820369482040405},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4404086470603943},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.42349809408187866},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.41343507170677185},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.4107424318790436},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4069674015045166},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.36071860790252686},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3534042537212372},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3420169949531555}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7033357620239258},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6694031953811646},{"id":"https://openalex.org/C53442348","wikidata":"https://www.wikidata.org/wiki/Q745101","display_name":"Content-addressable memory","level":3,"score":0.5767549872398376},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5142955183982849},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5081596374511719},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4820369482040405},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4404086470603943},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.42349809408187866},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.41343507170677185},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4107424318790436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4069674015045166},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.36071860790252686},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3534042537212372},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3420169949531555},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aipr.2017.8457937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2017.8457937","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1573503290","https://openalex.org/W1849277567","https://openalex.org/W1988115241","https://openalex.org/W2083758028","https://openalex.org/W2095425517","https://openalex.org/W2103496339","https://openalex.org/W2112796928","https://openalex.org/W2129217160","https://openalex.org/W2163605009"],"related_works":["https://openalex.org/W2068608913","https://openalex.org/W4293226380","https://openalex.org/W3124914020","https://openalex.org/W2141033859","https://openalex.org/W2077542787","https://openalex.org/W2156434174","https://openalex.org/W2071701083","https://openalex.org/W2383687187","https://openalex.org/W2081517010","https://openalex.org/W2121496884"],"abstract_inverted_index":{"It":[0],"is":[1,52,104,163],"true":[2],"there":[3],"have":[4],"been":[5,42],"great":[6],"improvements":[7,17],"with":[8,62],"the":[9,20,46,53,57,85,114,140,146,191,194,203],"effectiveness":[10],"of":[11,36,55,131,143,160,185,193,209],"utilizing":[12],"Neural":[13],"Networks.":[14],"However,":[15,39],"these":[16],"are,":[18],"for":[19,80],"most":[21],"part,":[22],"relegated":[23],"to":[24,64,139,150,200],"improved":[25],"clock":[26],"speeds,":[27],"leveraging":[28],"increase":[29],"in":[30,66,76,97,101,148],"memory,":[31],"and":[32,68,133,153,178,187,206],"GPU":[33],"enabled":[34],"parallelization":[35],"up-front":[37],"processing.":[38],"what":[40],"has":[41],"seemingly":[43],"forgotten":[44],"over":[45],"last":[47],"twenty":[48],"or":[49],"so":[50],"years":[51],"understanding":[54],"how":[56,199],"internal":[58,86],"layers":[59,72,88,144],"are":[60,89],"reacting":[61],"respect":[63],"convergence":[65],"training,":[67],"information":[69],"transformation":[70],"across":[71],"during":[73],"test,":[74],"which":[75],"turn":[77],"may":[78],"account":[79],"a":[81,118,175],"common":[82],"perception":[83],"that":[84,100,164],"neural":[87,121,195],"opaque":[90],"black":[91,204],"boxes.":[92],"This":[93],"paper":[94],"will":[95,109,125,197],"show":[96],"two":[98],"parts":[99],"fact,":[102],"this":[103],"not":[105],"true.":[106],"Part":[107,123,161],"one":[108],"demonstrate,":[110],"through":[111],"matrix":[112,136],"visualization,":[113],"feed-forward":[115],"processing":[116],"throughout":[117],"multi-layer":[119],"convolutional":[120],"network.":[122],"2":[124,162],"discuss":[126],"our":[127,165],"unique":[128],"derivative":[129],"application":[130],"Kohonen's":[132],"Kosko's":[134],"correlation":[135],"memory":[137,156],"methods":[138],"consecutive":[141],"pairs":[142],"within":[145],"network":[147,196],"order":[149],"form":[151],"stabilized":[152,166],"compressible":[154],"associative":[155],"matrices.":[157],"The":[158,181],"subtlety":[159],"matrices":[167],"can":[168],"be":[169],"simply":[170],"multiplied":[171],"together,":[172],"thus":[173],"forming":[174],"single":[176],"layer,":[177],"therefore":[179],"realizing":[180],"Universal":[182],"Approximation":[183],"Theorem":[184],"Cybenko":[186],"Hornik.":[188],"In":[189],"effect,":[190],"anatomy":[192],"reveal":[198],"open":[201],"up":[202],"box":[205],"take":[207],"advantage":[208],"its":[210],"inner":[211],"workings.":[212]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
