{"id":"https://openalex.org/W2766554021","doi":"https://doi.org/10.1007/978-3-319-70093-9_66","title":"Learning a Continuous Attractor Neural Network from Real Images","display_name":"Learning a Continuous Attractor Neural Network from Real Images","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2766554021","doi":"https://doi.org/10.1007/978-3-319-70093-9_66","mag":"2766554021"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-319-70093-9_66","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319965","https://openalex.org/P4310319900"],"host_organization_lineage_names":["Springer Nature","Springer Science+Business Media"],"type":"book series"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"book-chapter","type_crossref":"book-chapter","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/A5102991629","display_name":"Xiaolong Zou","orcid":"https://orcid.org/0000-0001-9397-6480"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolong Zou","raw_affiliation_strings":["School of Systems Science, Beijing Normal University, Beijing, 100875, China"],"affiliations":[{"raw_affiliation_string":"School of Systems Science, Beijing Normal University, Beijing, 100875, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021164660","display_name":"Zilong Ji","orcid":"https://orcid.org/0000-0001-7868-6178"},"institutions":[{"id":"https://openalex.org/I4210115644","display_name":"Chinese Institute for Brain Research","ror":"https://ror.org/029819q61","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210115644"]},{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zilong Ji","raw_affiliation_strings":["State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China","institution_ids":["https://openalex.org/I4210115644","https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100441062","display_name":"Xiao Liu","orcid":"https://orcid.org/0000-0002-2855-7367"},"institutions":[{"id":"https://openalex.org/I4210115644","display_name":"Chinese Institute for Brain Research","ror":"https://ror.org/029819q61","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210115644"]},{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Liu","raw_affiliation_strings":["State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China","institution_ids":["https://openalex.org/I4210115644","https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005708361","display_name":"Yuanyuan Mi","orcid":"https://orcid.org/0000-0003-3456-523X"},"institutions":[],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Mi","raw_affiliation_strings":["Brain Science Center, Institute of Basic Medical Sciences, Beijing, 100850, China"],"affiliations":[{"raw_affiliation_string":"Brain Science Center, Institute of Basic Medical Sciences, Beijing, 100850, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081204536","display_name":"K. Y. Michael Wong","orcid":"https://orcid.org/0000-0002-3078-4577"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"K. Y. Michael Wong","raw_affiliation_strings":["Department of Physics, Hong Kong University of Science and Technology, Kowloon, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Department of Physics, Hong Kong University of Science and Technology, Kowloon, Hong Kong","institution_ids":["https://openalex.org/I200769079","https://openalex.org/I889458895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106482748","display_name":"Si Wu","orcid":"https://orcid.org/0000-0001-9650-6935"},"institutions":[{"id":"https://openalex.org/I4210115644","display_name":"Chinese Institute for Brain Research","ror":"https://ror.org/029819q61","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210115644"]},{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Si Wu","raw_affiliation_strings":["State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China","institution_ids":["https://openalex.org/I4210115644","https://openalex.org/I25254941"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392,"provenance":"doaj"},"apc_paid":null,"fwci":1.656,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":6,"citation_normalized_percentile":{"value":0.856802,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":81,"max":83},"biblio":{"volume":null,"issue":null,"first_page":"622","last_page":"631"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10581","display_name":"Neuronal Oscillations in Cortical Networks","score":0.9985,"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"}},"topics":[{"id":"https://openalex.org/T10581","display_name":"Neuronal Oscillations in Cortical Networks","score":0.9985,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Network Fundamentals and Applications","score":0.9952,"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/T12859","display_name":"Advanced Techniques in Bioimage Analysis and Microscopy","score":0.9868,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.50707746},{"id":"https://openalex.org/keywords/backpropagation-learning","display_name":"Backpropagation Learning","score":0.506857},{"id":"https://openalex.org/keywords/cellular-imaging","display_name":"Cellular Imaging","score":0.504758},{"id":"https://openalex.org/keywords/working-memory","display_name":"Working Memory","score":0.502723},{"id":"https://openalex.org/keywords/recurrent-neural-networks","display_name":"Recurrent Neural Networks","score":0.500681},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4897766},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.44160646},{"id":"https://openalex.org/keywords/physical-neural-network","display_name":"Physical neural network","score":0.41449007}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7742797},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7476138},{"id":"https://openalex.org/C164380108","wikidata":"https://www.wikidata.org/wiki/Q507187","display_name":"Attractor","level":2,"score":0.7275641},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.62060815},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6176018},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.5835615},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.50707746},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4897766},{"id":"https://openalex.org/C173079777","wikidata":"https://www.wikidata.org/wiki/Q4299350","display_name":"Nervous system network models","level":5,"score":0.48377177},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4508351},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.44160646},{"id":"https://openalex.org/C33766855","wikidata":"https://www.wikidata.org/wiki/Q7189618","display_name":"Physical neural network","level":5,"score":0.41449007},{"id":"https://openalex.org/C177973122","wikidata":"https://www.wikidata.org/wiki/Q7860946","display_name":"Types of artificial neural networks","level":4,"score":0.32281262},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14901724},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-319-70093-9_66","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319965","https://openalex.org/P4310319900"],"host_organization_lineage_names":["Springer Nature","Springer Science+Business Media"],"type":"book series"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":20,"referenced_works":["https://openalex.org/W1503270516","https://openalex.org/W1686810756","https://openalex.org/W1988013646","https://openalex.org/W1994363342","https://openalex.org/W2022401803","https://openalex.org/W2029374903","https://openalex.org/W2039887166","https://openalex.org/W2047125104","https://openalex.org/W2054255785","https://openalex.org/W2054807558","https://openalex.org/W2058616551","https://openalex.org/W2069412624","https://openalex.org/W2128084896","https://openalex.org/W2128283823","https://openalex.org/W2155547196","https://openalex.org/W2165443127","https://openalex.org/W2170363883","https://openalex.org/W2260407442","https://openalex.org/W2424347275","https://openalex.org/W2610861664"],"related_works":["https://openalex.org/W4387656273","https://openalex.org/W4292953721","https://openalex.org/W3109717595","https://openalex.org/W2950022897","https://openalex.org/W2765937093","https://openalex.org/W180587397","https://openalex.org/W1595652908","https://openalex.org/W1584270863","https://openalex.org/W1538606284","https://openalex.org/W1538193578"],"abstract_inverted_index":{"Continuous":[0],"attractor":[1],"neural":[2,14,23,44,72,84,109,122,152],"networks":[3],"(CANNs)":[4],"have":[5],"been":[6],"widely":[7],"used":[8],"as":[9,166],"a":[10,27,38,48,82,88,107,113,134,148,155,167,177],"canonical":[11],"model":[12],"for":[13,42],"information":[15],"representation.":[16],"It":[17],"remains,":[18],"however,":[19],"unclear":[20],"how":[21],"the":[22,33,43,69,75,119,125,129,142,151,174,183],"system":[24,45,153],"acquires":[25],"such":[26,67],"network":[28,85,184],"structure":[29],"in":[30,106,158,182],"practice.":[31],"In":[32],"present":[34],"study,":[35],"we":[36],"propose":[37],"biological":[39],"plausible":[40],"scheme":[41,54],"to":[46,61,94,101,138],"learn":[47,102],"CANN":[49,156],"from":[50],"real":[51],"images.":[52],"The":[53,98],"contains":[55],"two":[56],"key":[57],"issues.":[58],"One":[59],"is":[60,100],"generate":[62],"high-level":[63],"representations":[64,73,123],"of":[65,91,128,136,160,170,173,179],"objects,":[66],"that":[68,140,161],"correlation":[70,120],"between":[71,78,121],"reflects":[74],"sematic":[76],"relationship":[77],"objects.":[79],"We":[80,111,131],"adopt":[81,112],"deep":[83],"trained":[86],"by":[87,147],"large":[89],"number":[90,135],"natural":[92],"images":[93,144,163],"achieve":[95],"this":[96],"goal.":[97],"other":[99],"correlated":[103],"memory":[104],"patterns":[105],"recurrent":[108],"network.":[110,130],"modified":[114],"Hebb":[115],"rule,":[116],"which":[117],"encodes":[118],"into":[124],"connection":[126],"form":[127],"carry":[132],"out":[133],"experiments":[137],"demonstrate":[139],"when":[141],"presented":[143],"are":[145,164],"linked":[146],"continuous":[149,168],"feature,":[150],"learns":[154],"successfully,":[157],"term":[159],"these":[162],"stored":[165],"family":[169],"stationary":[171],"states":[172],"network,":[175],"forming":[176],"sub-manifold":[178],"low":[180],"energy":[181],"state":[185],"space.":[186]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2766554021","counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2024-11-22T23:01:37.833568","created_date":"2017-11-10"}