{"id":"https://openalex.org/W3212726427","doi":"https://doi.org/10.1109/access.2021.3126311","title":"Spiking Neural Network Discovers Energy-Efficient Hexapod Motion in Deep Reinforcement Learning","display_name":"Spiking Neural Network Discovers Energy-Efficient Hexapod Motion in Deep Reinforcement Learning","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3212726427","doi":"https://doi.org/10.1109/access.2021.3126311","mag":"3212726427"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3126311","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3126311","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2021.3126311","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077993533","display_name":"Katsumi Naya","orcid":"https://orcid.org/0000-0002-4102-862X"},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Katsumi Naya","raw_affiliation_strings":["Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan"],"raw_orcid":"https://orcid.org/0000-0002-4102-862X","affiliations":[{"raw_affiliation_string":"Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan","institution_ids":["https://openalex.org/I201537933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058240917","display_name":"Kyo Kutsuzawa","orcid":"https://orcid.org/0000-0002-5326-7847"},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kyo Kutsuzawa","raw_affiliation_strings":["Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan"],"raw_orcid":"https://orcid.org/0000-0002-5326-7847","affiliations":[{"raw_affiliation_string":"Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan","institution_ids":["https://openalex.org/I201537933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072945772","display_name":"Dai Owaki","orcid":"https://orcid.org/0000-0003-1217-3892"},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Dai Owaki","raw_affiliation_strings":["Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan"],"raw_orcid":"https://orcid.org/0000-0003-1217-3892","affiliations":[{"raw_affiliation_string":"Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan","institution_ids":["https://openalex.org/I201537933"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060102825","display_name":"Mitsuhiro Hayashibe","orcid":"https://orcid.org/0000-0001-6179-5706"},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Mitsuhiro Hayashibe","raw_affiliation_strings":["Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan. (e-mail: mitsuhiro.hayashibe@inria.fr)","Department of Robotics, Graduate School of Engineering, Neuro-Robotics Laboratory, Tohoku University, Sendai, Japan"],"raw_orcid":"https://orcid.org/0000-0001-6179-5706","affiliations":[{"raw_affiliation_string":"Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan. (e-mail: mitsuhiro.hayashibe@inria.fr)","institution_ids":["https://openalex.org/I201537933"]},{"raw_affiliation_string":"Department of Robotics, Graduate School of Engineering, Neuro-Robotics Laboratory, Tohoku University, Sendai, Japan","institution_ids":["https://openalex.org/I201537933"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5077993533"],"corresponding_institution_ids":["https://openalex.org/I201537933"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.9152,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.74608357,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"9","issue":null,"first_page":"150345","last_page":"150354"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9987000226974487,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9955999851226807,"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/hexapod","display_name":"Hexapod","score":0.8691835403442383},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7484830021858215},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.729814350605011},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.5680004954338074},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.5550200343132019},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5422071814537048},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.5357438921928406},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5341687798500061},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5220794081687927},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4829120337963104},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.42809027433395386},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.4136233329772949},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15218451619148254},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08054500818252563}],"concepts":[{"id":"https://openalex.org/C136434205","wikidata":"https://www.wikidata.org/wiki/Q3437269","display_name":"Hexapod","level":3,"score":0.8691835403442383},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7484830021858215},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.729814350605011},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.5680004954338074},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.5550200343132019},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5422071814537048},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.5357438921928406},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5341687798500061},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5220794081687927},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4829120337963104},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.42809027433395386},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.4136233329772949},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15218451619148254},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08054500818252563},{"id":"https://openalex.org/C42407357","wikidata":"https://www.wikidata.org/wiki/Q521","display_name":"Physiology","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3126311","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3126311","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:335681a7613746e5a4b027d670d343bf","is_oa":true,"landing_page_url":"https://doaj.org/article/335681a7613746e5a4b027d670d343bf","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 150345-150354 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3126311","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3126311","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8999999761581421,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G6760651939","display_name":null,"funder_award_id":"20H05458","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W59997565","https://openalex.org/W1530318945","https://openalex.org/W1985940938","https://openalex.org/W2021538146","https://openalex.org/W2054747732","https://openalex.org/W2072700524","https://openalex.org/W2109596721","https://openalex.org/W2130360162","https://openalex.org/W2157239334","https://openalex.org/W2158782408","https://openalex.org/W2173248099","https://openalex.org/W2513853720","https://openalex.org/W2620474507","https://openalex.org/W2621826044","https://openalex.org/W2781726626","https://openalex.org/W2787938642","https://openalex.org/W2907516714","https://openalex.org/W2960575309","https://openalex.org/W2963654596","https://openalex.org/W2963864421","https://openalex.org/W2963923407","https://openalex.org/W2969489794","https://openalex.org/W2994240296","https://openalex.org/W2998534312","https://openalex.org/W3001803449","https://openalex.org/W3003876377","https://openalex.org/W3018463921","https://openalex.org/W3026167732","https://openalex.org/W3036619998","https://openalex.org/W3093146707","https://openalex.org/W3102087395","https://openalex.org/W3129045599","https://openalex.org/W3130821023","https://openalex.org/W3157836284","https://openalex.org/W3210492965","https://openalex.org/W6602394311","https://openalex.org/W6684921986","https://openalex.org/W6747473740","https://openalex.org/W6748839928","https://openalex.org/W6771522568","https://openalex.org/W6784189638","https://openalex.org/W6910476156"],"related_works":["https://openalex.org/W2146302595","https://openalex.org/W613729187","https://openalex.org/W138543751","https://openalex.org/W2789574804","https://openalex.org/W2114803124","https://openalex.org/W2063353256","https://openalex.org/W123722128","https://openalex.org/W4390269031","https://openalex.org/W4389787393","https://openalex.org/W2969223576"],"abstract_inverted_index":{"In":[0,77],"Deep":[1,228,240],"Reinforcement":[2],"Learning":[3],"(DRL)":[4],"for":[5,44,149,190,215,252,256,260],"robotics":[6],"application,":[7],"it":[8,92],"is":[9,21,41,53,60,82,93,113,120,129],"important":[10],"to":[11,22,30,95,131,177],"find":[12,31],"energy-efficient":[13,202],"motions.":[14],"For":[15],"this":[16],"purpose,":[17],"a":[18,54,125,161,195,210,222,235,247],"standard":[19],"method":[20,40,127],"set":[23],"an":[24,191,201],"action":[25],"penalty":[26,59,81,101,133,168,197],"in":[27,170,221,234,246,267],"the":[28,32,36,45,51,58,63,80,85,97,100,104,110,118,132,139,166,171,178,186],"reward":[29,52],"optimal":[33,192],"motion":[34],"considering":[35],"energy":[37,167,213,270],"expenditure.":[38],"This":[39],"widely":[42],"used":[43],"simplicity":[46],"of":[47,99,207,212,224,237,249],"implementation.":[48],"However,":[49],"since":[50,116],"linear":[55],"sum,":[56],"if":[57,79],"too":[61,83],"large,":[62],"system":[64],"will":[65],"fall":[66],"into":[67],"local":[68],"minima":[69],"and":[70,109,153,199,239,258],"no":[71],"moving":[72],"solution":[73],"can":[74],"be":[75,89],"obtained.":[76],"contrast,":[78],"small,":[84],"effect":[86,112],"may":[87],"not":[88],"sufficient.":[90,114],"Therefore,":[91],"necessary":[94],"adjust":[96],"amount":[98],"so":[102],"that":[103,128],"agent":[105,163,187],"always":[106],"moves":[107],"dynamically,":[108],"energy-saving":[111],"Nevertheless,":[115],"adjusting":[117],"hyperparameters":[119],"computationally":[121],"expensive,":[122],"we":[123,183],"need":[124],"learning":[126,268],"robust":[130],"setting":[134],"problem.":[135],"We":[136,156],"investigated":[137],"on":[138],"Spiking":[140],"Neural":[141],"Network":[142],"(SNN),":[143],"which":[144],"has":[145],"been":[146],"attracting":[147],"attention":[148],"its":[150],"computational":[151],"efficiency":[152,214],"neuromorphic":[154],"architecture.":[155],"conducted":[157],"gait":[158,193,203,272],"experiments":[159],"using":[160],"hexapod":[162],"while":[164],"varying":[165],"settings":[169],"simulation":[172],"environment.":[173],"By":[174],"applying":[175],"SNN":[176,265],"conventional":[179],"state-of-the-art":[180],"DRL":[181,262],"algorithms,":[182],"examined":[184],"whether":[185],"could":[188],"explore":[189],"with":[194,205,264,273],"larger":[196],"variation":[198],"obtain":[200],"verified":[204],"Cost":[206],"Transport":[208],"(CoT),":[209],"metric":[211],"gait.":[216],"Soft":[217],"Actor-Critic":[218],"(SAC)+SNN":[219],"resulted":[220,233,245],"CoT":[223,236,248],"1.64,":[225],"Twin":[226],"Delayed":[227],"Deterministic":[229,241],"policy":[230,242],"gradient":[231,243],"(TD3)+SNN":[232],"2.21,":[238],"(DDPG)+SNN":[244],"2.08":[250],"(1.91":[251],"normal":[253],"SAC,":[254],"2.38":[255],"TD3,":[257],"2.40":[259],"DDPG).":[261],"combined":[263],"succeeded":[266],"more":[269],"efficient":[271],"lower":[274],"CoT.":[275]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2026-05-07T13:39:58.223016","created_date":"2025-10-10T00:00:00"}