{"id":"https://openalex.org/W3187181453","doi":"https://doi.org/10.1109/cec45853.2021.9504921","title":"Exploration Of Encoding And Decoding Methods For Spiking Neural Networks On The Cart Pole And Lunar Lander Problems Using Evolutionary Training","display_name":"Exploration Of Encoding And Decoding Methods For Spiking Neural Networks On The Cart Pole And Lunar Lander Problems Using Evolutionary Training","publication_year":2021,"publication_date":"2021-06-28","ids":{"openalex":"https://openalex.org/W3187181453","doi":"https://doi.org/10.1109/cec45853.2021.9504921","mag":"3187181453"},"language":"en","primary_location":{"id":"doi:10.1109/cec45853.2021.9504921","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec45853.2021.9504921","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/conference_contribution/Exploration_Of_Encoding_And_Decoding_Methods_For_Spiking_Neural_Networks_On_The_Cart_Pole_And_Lunar_Lander_Problems_Using_Evolutionary_Training/24024024","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027153071","display_name":"Andrew W. Rafe","orcid":null},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Andrew W. Rafe","raw_affiliation_strings":["School of Computer Science, University of Technology Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Technology Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084118011","display_name":"Jaime Garc\u00eda","orcid":"https://orcid.org/0000-0001-5718-1605"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jaime A. Garcia","raw_affiliation_strings":["School of Computer Science, University of Technology Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Technology Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075906421","display_name":"William Raffe","orcid":"https://orcid.org/0000-0001-5310-0943"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"William L. Raffe","raw_affiliation_strings":["School of Computer Science, University of Technology Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Technology Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5027153071"],"corresponding_institution_ids":["https://openalex.org/I114017466"],"apc_list":null,"apc_paid":null,"fwci":0.1015,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.42946314,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"498","last_page":"505"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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":1.0,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9983000159263611,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9940999746322632,"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/decoding-methods","display_name":"Decoding methods","score":0.860284686088562},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.753199577331543},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.7406948804855347},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6140039563179016},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.609980046749115},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5922445058822632},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5900725722312927},{"id":"https://openalex.org/keywords/neural-decoding","display_name":"Neural decoding","score":0.5485873222351074},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5246334671974182},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.47755321860313416},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.4448612630367279},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4216848611831665},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2079736888408661},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09207355976104736}],"concepts":[{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.860284686088562},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.753199577331543},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.7406948804855347},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6140039563179016},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.609980046749115},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5922445058822632},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5900725722312927},{"id":"https://openalex.org/C40743351","wikidata":"https://www.wikidata.org/wiki/Q7002049","display_name":"Neural decoding","level":3,"score":0.5485873222351074},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5246334671974182},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.47755321860313416},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.4448612630367279},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4216848611831665},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2079736888408661},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09207355976104736},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cec45853.2021.9504921","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec45853.2021.9504921","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"},{"id":"pmh:oai:figshare.com:article/24024024","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Exploration_Of_Encoding_And_Decoding_Methods_For_Spiking_Neural_Networks_On_The_Cart_Pole_And_Lunar_Lander_Problems_Using_Evolutionary_Training/24024024","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/24024024","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Exploration_Of_Encoding_And_Decoding_Methods_For_Spiking_Neural_Networks_On_The_Cart_Pole_And_Lunar_Lander_Problems_Using_Evolutionary_Training/24024024","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W22297218","https://openalex.org/W1985940938","https://openalex.org/W2022908762","https://openalex.org/W2109596721","https://openalex.org/W2112796928","https://openalex.org/W2141395782","https://openalex.org/W2146461161","https://openalex.org/W2152637387","https://openalex.org/W2164653071","https://openalex.org/W2513853720","https://openalex.org/W2621826044","https://openalex.org/W2753327670","https://openalex.org/W2890344110","https://openalex.org/W2896936049","https://openalex.org/W2994602346","https://openalex.org/W6780559895"],"related_works":["https://openalex.org/W2977464668","https://openalex.org/W4283822356","https://openalex.org/W1950940422","https://openalex.org/W2129146436","https://openalex.org/W2032507829","https://openalex.org/W2147282173","https://openalex.org/W2045321573","https://openalex.org/W2005847692","https://openalex.org/W2947588442","https://openalex.org/W4309394452"],"abstract_inverted_index":{"Spiking":[0,100],"Neural":[1,29,101],"Networks":[2],"are":[3,32],"increasingly":[4],"drawing":[5],"interest":[6],"due":[7,34,160],"to":[8,23,35,130,152,161,167,189],"their":[9,36,190],"potential":[10],"for":[11,44],"large":[12],"efficiency":[13],"gains":[14],"when":[15,21],"used":[16],"with":[17],"neuromorphic":[18],"computers.":[19],"However,":[20],"attempting":[22],"replicate":[24],"the":[25,54,62,70,82,104,112,128,132,135,154,162,174,197,208,213,216],"successes":[26],"of":[27,53,64,72,78,85,97,156,215],"Artificial":[28],"Networks,":[30],"challenges":[31],"faced":[33],"vastly":[37],"different":[38],"architectures":[39],"and":[40,46,58,61,91,115,134,178],"therefore":[41],"differing":[42],"methods":[43,60,143,164],"training":[45],"optimisation.":[47],"There":[48],"has":[49],"been":[50],"minimal":[51],"analysis":[52,84],"differences":[55,175],"between":[56,127,193],"encoding":[57,95],"decoding":[59,86,142,163,180],"effect":[63],"state":[65,88,136,146,205],"space":[66,137],"exposure":[67,89,138,147],"periods":[68],"on":[69],"performance":[71],"these":[73],"networks.":[74],"The":[75,121,145],"core":[76],"contribution":[77],"this":[79],"paper":[80,122,172],"is":[81,109,149,201],"detailed":[83],"methods,":[87],"periods,":[90],"a":[92,124],"learned":[93],"input":[94,199],"method":[96],"an":[98,169],"evolved":[99],"Network":[102],"within":[103],"Reinforcement":[105,118],"Learning":[106,119],"context.":[107],"This":[108,171],"demonstrated":[110],"using":[111,207],"Cart":[113],"Pole":[114],"Lunar":[116],"Lander":[117],"problems.":[120],"discovers":[123],"negative":[125],"correlation":[126],"generation":[129],"reach":[131],"goal":[133],"period":[139,148],"over":[140],"all":[141],"tested.":[144],"also":[150],"found":[151],"influence":[153],"number":[155],"random":[157],"actions":[158],"taken":[159],"being":[165],"unable":[166],"select":[168],"action.":[170],"explores":[173],"in":[176,186],"temporal":[177],"rate-based":[179],"as":[181,183,212],"well":[182],"identifying":[184],"benefits":[185],"resetting":[187],"networks":[188],"default":[191],"states":[192],"episode":[194],"steps.":[195],"Additionally,":[196],"novel":[198],"encoder,":[200],"effective":[202],"at":[203],"pre-processing":[204],"information":[206],"same":[209],"evolutionary":[210],"algorithm":[211],"rest":[214],"network.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
