{"id":"https://openalex.org/W3091817097","doi":"https://doi.org/10.1109/ecctd49232.2020.9218304","title":"Template Optimization in Cellular Neural Networks Using Gradient Based Approaches","display_name":"Template Optimization in Cellular Neural Networks Using Gradient Based Approaches","publication_year":2020,"publication_date":"2020-09-01","ids":{"openalex":"https://openalex.org/W3091817097","doi":"https://doi.org/10.1109/ecctd49232.2020.9218304","mag":"3091817097"},"language":"en","primary_location":{"id":"doi:10.1109/ecctd49232.2020.9218304","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ecctd49232.2020.9218304","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 European Conference on Circuit Theory and Design (ECCTD)","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/A5060426633","display_name":"Andr\u00e1s F\u00fcl\u00f6p","orcid":"https://orcid.org/0000-0003-0873-5020"},"institutions":[{"id":"https://openalex.org/I31882830","display_name":"P\u00e1zm\u00e1ny P\u00e9ter Catholic University","ror":"https://ror.org/05v9kya57","country_code":"HU","type":"education","lineage":["https://openalex.org/I31882830"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Andras Fulop","raw_affiliation_strings":["Faculty of Information Technology and Bionics, Pazmany Peter Catholic University, Budapest, Hungary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology and Bionics, Pazmany Peter Catholic University, Budapest, Hungary","institution_ids":["https://openalex.org/I31882830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070051531","display_name":"Andr\u00e1s Horv\u00e1th","orcid":"https://orcid.org/0000-0001-7325-5547"},"institutions":[{"id":"https://openalex.org/I31882830","display_name":"P\u00e1zm\u00e1ny P\u00e9ter Catholic University","ror":"https://ror.org/05v9kya57","country_code":"HU","type":"education","lineage":["https://openalex.org/I31882830"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Andras Horvath","raw_affiliation_strings":["Faculty of Information Technology and Bionics, Pazmany Peter Catholic University, Budapest, Hungary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology and Bionics, Pazmany Peter Catholic University, Budapest, Hungary","institution_ids":["https://openalex.org/I31882830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9724,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.78451452,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11347","display_name":"Neural Networks Stability and Synchronization","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11347","display_name":"Neural Networks Stability and Synchronization","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10320","display_name":"Neural Networks and Applications","score":0.9968000054359436,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9961000084877014,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cellular-neural-network","display_name":"Cellular neural network","score":0.8345138430595398},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.798599123954773},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7356510758399963},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6056562662124634},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6032052636146545},{"id":"https://openalex.org/keywords/template","display_name":"Template","score":0.533953070640564},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5068371891975403},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5015408992767334},{"id":"https://openalex.org/keywords/cellular-network","display_name":"Cellular network","score":0.4575776755809784},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3922422528266907},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36114758253097534},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.0686282217502594}],"concepts":[{"id":"https://openalex.org/C812465","wikidata":"https://www.wikidata.org/wiki/Q5058375","display_name":"Cellular neural network","level":3,"score":0.8345138430595398},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.798599123954773},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7356510758399963},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6056562662124634},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6032052636146545},{"id":"https://openalex.org/C82714645","wikidata":"https://www.wikidata.org/wiki/Q438331","display_name":"Template","level":2,"score":0.533953070640564},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5068371891975403},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5015408992767334},{"id":"https://openalex.org/C153646914","wikidata":"https://www.wikidata.org/wiki/Q535695","display_name":"Cellular network","level":2,"score":0.4575776755809784},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3922422528266907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36114758253097534},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0686282217502594},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ecctd49232.2020.9218304","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ecctd49232.2020.9218304","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 European Conference on Circuit Theory and Design (ECCTD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W197865394","https://openalex.org/W225768461","https://openalex.org/W280536263","https://openalex.org/W1597009674","https://openalex.org/W1836465849","https://openalex.org/W2131774270","https://openalex.org/W2145023731","https://openalex.org/W2151162785","https://openalex.org/W2163605009","https://openalex.org/W2531409750","https://openalex.org/W2554314924","https://openalex.org/W2612915522","https://openalex.org/W2618530766","https://openalex.org/W2620369751","https://openalex.org/W2798376494","https://openalex.org/W2895580425","https://openalex.org/W2949117887","https://openalex.org/W2977266164","https://openalex.org/W2997287227","https://openalex.org/W4250482878","https://openalex.org/W4293320219","https://openalex.org/W6604653223","https://openalex.org/W6635935089","https://openalex.org/W6638667902","https://openalex.org/W6729767818"],"related_works":["https://openalex.org/W2121300814","https://openalex.org/W4234406076","https://openalex.org/W1886613375","https://openalex.org/W4250583430","https://openalex.org/W2010731026","https://openalex.org/W2082337425","https://openalex.org/W206608699","https://openalex.org/W2024633643","https://openalex.org/W2489260869","https://openalex.org/W2000135173"],"abstract_inverted_index":{"Cellular":[0],"neural":[1,17,46],"networks":[2,29,47,76],"were":[3,48],"used":[4,82],"with":[5,91],"success":[6],"in":[7,20,34,59],"the":[8,14,24,35],"past":[9],"decades":[10],"and":[11,32,77,86,93],"helped":[12,33],"laying":[13],"foundations":[15],"of":[16,37,44],"net-work":[18],"applications":[19],"image":[21],"processing.":[22],"In":[23,62],"last":[25],"few":[26],"years":[27],"convolutional":[28,60],"have":[30],"appeared":[31],"solution":[36],"complex":[38],"practical":[39],"problems.":[40],"Meanwhile":[41],"programming":[42],"templates":[43],"cellular":[45,75],"designed":[49],"by":[50],"analytical":[51],"methods,":[52],"gradient":[53],"based":[54],"optimization":[55],"is":[56],"applied":[57],"popularly":[58],"networks.":[61],"this":[63],"paper":[64],"we":[65],"will":[66],"demonstrate":[67],"how":[68,78],"these":[69],"methods":[70],"can":[71,80],"be":[72,81],"exploited":[73],"using":[74],"they":[79],"to":[83],"implement":[84],"classification":[85],"feature":[87],"extraction":[88],"tasks,":[89],"both":[90],"standard":[92],"memristive":[94],"cell":[95],"dynamics.":[96]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}