{"id":"https://openalex.org/W7125374434","doi":"https://doi.org/10.1145/3784828.3786360","title":"A Unidirectional Two-Compartment Neuron Circuit with On-chip STDP learning","display_name":"A Unidirectional Two-Compartment Neuron Circuit with On-chip STDP learning","publication_year":2026,"publication_date":"2026-01-22","ids":{"openalex":"https://openalex.org/W7125374434","doi":"https://doi.org/10.1145/3784828.3786360"},"language":null,"primary_location":{"id":"doi:10.1145/3784828.3786360","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3784828.3786360","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Supercomputing Asia and International Conference on High Performance Computing in Asia Pacific Region Workshops","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3784828.3786360","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123556620","display_name":"Ashish Gautam","orcid":null},"institutions":[{"id":"https://openalex.org/I1289243028","display_name":"Oak Ridge National Laboratory","ror":"https://ror.org/01qz5mb56","country_code":"US","type":"facility","lineage":["https://openalex.org/I1289243028","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I4210159294"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ashish Gautam","raw_affiliation_strings":["Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA"],"raw_orcid":"https://orcid.org/0000-0002-4799-7739","affiliations":[{"raw_affiliation_string":"Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA","institution_ids":["https://openalex.org/I1289243028"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121531268","display_name":"Shunta Furuichi","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shunta Furuichi","raw_affiliation_strings":["Institute of Industrial Science, The University of Tokyo, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0009-0000-3660-0030","affiliations":[{"raw_affiliation_string":"Institute of Industrial Science, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5121573377","display_name":"Takashi Kohno","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takashi Kohno","raw_affiliation_strings":["Institute of Industrial Science, The University of Tokyo, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0002-7518-3636","affiliations":[{"raw_affiliation_string":"Institute of Industrial Science, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06262803,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"348","last_page":"352"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.960099995136261,"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":0.960099995136261,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.02329999953508377,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.00570000009611249,"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/soma","display_name":"Soma","score":0.7628999948501587},{"id":"https://openalex.org/keywords/current-conveyor","display_name":"Current conveyor","score":0.5656999945640564},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.4878000020980835},{"id":"https://openalex.org/keywords/electronic-circuit","display_name":"Electronic circuit","score":0.4578000009059906},{"id":"https://openalex.org/keywords/synapse","display_name":"Synapse","score":0.43720000982284546},{"id":"https://openalex.org/keywords/resistor","display_name":"Resistor","score":0.41019999980926514},{"id":"https://openalex.org/keywords/transistor","display_name":"Transistor","score":0.3849000036716461},{"id":"https://openalex.org/keywords/capacitance","display_name":"Capacitance","score":0.38269999623298645},{"id":"https://openalex.org/keywords/neuron","display_name":"Neuron","score":0.35989999771118164}],"concepts":[{"id":"https://openalex.org/C2779617337","wikidata":"https://www.wikidata.org/wiki/Q842429","display_name":"Soma","level":2,"score":0.7628999948501587},{"id":"https://openalex.org/C110252649","wikidata":"https://www.wikidata.org/wiki/Q5195095","display_name":"Current conveyor","level":4,"score":0.5656999945640564},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.551800012588501},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.4878000020980835},{"id":"https://openalex.org/C134146338","wikidata":"https://www.wikidata.org/wiki/Q1815901","display_name":"Electronic circuit","level":2,"score":0.4578000009059906},{"id":"https://openalex.org/C127445978","wikidata":"https://www.wikidata.org/wiki/Q187181","display_name":"Synapse","level":2,"score":0.43720000982284546},{"id":"https://openalex.org/C137488568","wikidata":"https://www.wikidata.org/wiki/Q5321","display_name":"Resistor","level":3,"score":0.41019999980926514},{"id":"https://openalex.org/C172385210","wikidata":"https://www.wikidata.org/wiki/Q5339","display_name":"Transistor","level":3,"score":0.3849000036716461},{"id":"https://openalex.org/C30066665","wikidata":"https://www.wikidata.org/wiki/Q164399","display_name":"Capacitance","level":3,"score":0.38269999623298645},{"id":"https://openalex.org/C2778794669","wikidata":"https://www.wikidata.org/wiki/Q43054","display_name":"Neuron","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.3458000123500824},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C530198007","wikidata":"https://www.wikidata.org/wiki/Q80831","display_name":"Integrated circuit","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C154318817","wikidata":"https://www.wikidata.org/wiki/Q2157249","display_name":"Parasitic capacitance","level":4,"score":0.3192000091075897},{"id":"https://openalex.org/C188058453","wikidata":"https://www.wikidata.org/wiki/Q1815901","display_name":"Discrete circuit","level":4,"score":0.3127000033855438},{"id":"https://openalex.org/C52192207","wikidata":"https://www.wikidata.org/wiki/Q5322","display_name":"Capacitor","level":3,"score":0.3050999939441681},{"id":"https://openalex.org/C173966970","wikidata":"https://www.wikidata.org/wiki/Q786012","display_name":"Current mirror","level":4,"score":0.29820001125335693},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.28690001368522644},{"id":"https://openalex.org/C2779283907","wikidata":"https://www.wikidata.org/wiki/Q1632964","display_name":"Transconductance","level":4,"score":0.2838999927043915},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.2786000072956085},{"id":"https://openalex.org/C2908872077","wikidata":"https://www.wikidata.org/wiki/Q1187365","display_name":"Electrical Synapses","level":4,"score":0.27410000562667847},{"id":"https://openalex.org/C118403218","wikidata":"https://www.wikidata.org/wiki/Q43283","display_name":"Biological neural network","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C46362747","wikidata":"https://www.wikidata.org/wiki/Q173431","display_name":"CMOS","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C50820595","wikidata":"https://www.wikidata.org/wiki/Q5256675","display_name":"Dendritic spike","level":4,"score":0.2531000077724457},{"id":"https://openalex.org/C150072547","wikidata":"https://www.wikidata.org/wiki/Q212923","display_name":"Memristor","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3784828.3786360","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3784828.3786360","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Supercomputing Asia and International Conference on High Performance Computing in Asia Pacific Region Workshops","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3784828.3786360","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3784828.3786360","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Supercomputing Asia and International Conference on High Performance Computing in Asia Pacific Region Workshops","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3049477530","display_name":null,"funder_award_id":"Contract No. DE-AC05-00OR22725","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"}],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1975398991","https://openalex.org/W2014059210","https://openalex.org/W2040407982","https://openalex.org/W2064971633","https://openalex.org/W2159217888","https://openalex.org/W2429655552","https://openalex.org/W2596998695","https://openalex.org/W2911439391","https://openalex.org/W3024907317","https://openalex.org/W3203183043","https://openalex.org/W4214639850","https://openalex.org/W4234546939","https://openalex.org/W4311886357","https://openalex.org/W4384155678"],"related_works":[],"abstract_inverted_index":{"Most":[0],"neuromorphic":[1],"chips":[2],"implement":[3],"the":[4,51,68,81,95,99,104,108,122,143,165,173,206],"single-compartment":[5,183],"point":[6],"neuron":[7,33,61,177,220],"model":[8],"where":[9,63],"synapse":[10,100,144],"circuits":[11,101,147],"connect":[12,142],"directly":[13],"to":[14,46,50,120,133,141,181],"a":[15,26,40,135,155,182,186,218],"leaky":[16],"integrate":[17],"and":[18,84,91,145,192,198,216],"fire":[19],"(LIF)":[20],"soma":[21,29,52,109,146],"circuit.":[22,53,221],"However,":[23],"when":[24],"using":[25],"biologically":[27,156,199],"plausible":[28,157],"circuit":[30,78,110,119,178,184],"(e.g.,":[31],"Hodgkin-Huxley":[32],"model),":[34],"an":[35,76,114,117,190],"interface":[36,77,118],"circuitry,":[37],"such":[38],"as":[39,189],"current":[41,49,86,151,187],"conveyor":[42,152,188],"circuit,":[43],"is":[44,55,66,179,195,203],"needed":[45],"transmit":[47],"synaptic":[48],"This":[54,73],"especially":[56],"true":[57],"for":[58,75],"ultra-low":[59],"power":[60],"circuits,":[62],"membrane":[64,124],"capacitance":[65,83],"on":[67],"order":[69],"of":[70,94,107,149,172],"20":[71],"fF.":[72],"need":[74],"arises":[79],"because":[80],"parasitic":[82],"leakage":[85],"caused":[87],"by":[88],"fabrication":[89],"mismatch":[90],"second-order":[92],"effects":[93],"output":[96],"transistors":[97],"in":[98,205],"can":[102],"disturb":[103],"spiking":[105],"dynamics":[106],"if":[111],"connected":[112],"without":[113],"interface.":[115],"Using":[116,154],"isolate":[121],"soma\u2019s":[123],"capacitor":[125],"from":[126],"synapses":[127],"resolves":[128],"this":[129],"issue.":[130],"We":[131],"propose":[132],"use":[134],"unidirectional":[136,175],"resistor":[137],"(a":[138],"transconductance":[139],"circuit)":[140],"instead":[148],"conventional":[150],"circuits.":[153],"spike":[158],"pattern":[159],"detection":[160],"model,":[161],"we":[162],"show":[163],"that":[164],"on-chip":[166],"spike-timing-dependent":[167],"plasticity":[168],"(STDP)":[169],"learning":[170],"performance":[171],"proposed":[174],"two-compartment":[176],"similar":[180],"(with":[185],"interface)":[191],"additionally,":[193],"it":[194],"more":[196],"power-efficient":[197],"plausible.":[200],"The":[201],"chip":[202],"fabricated":[204],"Taiwan":[207],"Semiconductor":[208],"Manufacturing":[209],"Company":[210],"(TSMC)":[211],"250":[212],"nm":[213],"technology":[214],"node":[215],"comprises":[217],"single":[219]},"counts_by_year":[],"updated_date":"2026-01-24T23:23:39.755997","created_date":"2026-01-23T00:00:00"}
