{"id":"https://openalex.org/W4392579296","doi":"https://doi.org/10.1162/neco_a_01648","title":"Frequency Propagation: Multimechanism Learning in Nonlinear Physical Networks","display_name":"Frequency Propagation: Multimechanism Learning in Nonlinear Physical Networks","publication_year":2024,"publication_date":"2024-03-08","ids":{"openalex":"https://openalex.org/W4392579296","doi":"https://doi.org/10.1162/neco_a_01648","pmid":"https://pubmed.ncbi.nlm.nih.gov/38457749"},"language":"en","primary_location":{"id":"doi:10.1162/neco_a_01648","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_01648","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718","https://openalex.org/P4310316440"],"host_organization_lineage_names":["The MIT Press","Massachusetts Institute of Technology"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5062619423","display_name":"Vidyesh Rao Anisetti","orcid":"https://orcid.org/0000-0002-2769-4773"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vidyesh Rao Anisetti","raw_affiliation_strings":["Physics Department, Syracuse University, Syracuse, NY 13244 U.S.A. vvaniset@syr.edu"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Physics Department, Syracuse University, Syracuse, NY 13244 U.S.A. vvaniset@syr.edu","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058428990","display_name":"Ananth Kandala","orcid":null},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ananth Kandala","raw_affiliation_strings":["Department of Physics, University of Florida, Gainesville, FL 32611, U.S.A. an.kandala@ufl.edu"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Physics, University of Florida, Gainesville, FL 32611, U.S.A. an.kandala@ufl.edu","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062849992","display_name":"Benjamin Scellier","orcid":null},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Benjamin Scellier","raw_affiliation_strings":["Department of Mathematics, ETH Z\u00fcrich, 8092 Z\u00fcrich, Switzerland benjamin.scellier@polytechnique.edu"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, ETH Z\u00fcrich, 8092 Z\u00fcrich, Switzerland benjamin.scellier@polytechnique.edu","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091701533","display_name":"J. M. Schwarz","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114425","display_name":"Indian Creek Farm","ror":"https://ror.org/029ah8167","country_code":"US","type":"other","lineage":["https://openalex.org/I4210114425"]},{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]},{"id":"https://openalex.org/I87424562","display_name":"Ithaca College","ror":"https://ror.org/01kw1gj07","country_code":"US","type":"education","lineage":["https://openalex.org/I87424562"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"J. M. Schwarz","raw_affiliation_strings":["Indian Creek Farm, Ithaca, NY 14850, U.S.A. jmschw02@syr.edu","Physics Department, Syracuse University, Syracuse, NY 13244 U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Creek Farm, Ithaca, NY 14850, U.S.A. jmschw02@syr.edu","institution_ids":["https://openalex.org/I4210114425","https://openalex.org/I87424562"]},{"raw_affiliation_string":"Physics Department, Syracuse University, Syracuse, NY 13244 U.S.A","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5058428990","https://openalex.org/A5062619423","https://openalex.org/A5062849992","https://openalex.org/A5091701533"],"corresponding_institution_ids":["https://openalex.org/I33213144","https://openalex.org/I35440088","https://openalex.org/I4210114425","https://openalex.org/I70983195","https://openalex.org/I87424562"],"apc_list":null,"apc_paid":null,"fwci":1.9642,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.85498641,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"36","issue":"4","first_page":"596","last_page":"620"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10923","display_name":"Force Microscopy Techniques and Applications","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10923","display_name":"Force Microscopy Techniques and Applications","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9860000014305115,"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":0.9840999841690063,"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/gradient-descent","display_name":"Gradient descent","score":0.6253525018692017},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5903246998786926},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5747908353805542},{"id":"https://openalex.org/keywords/superposition-principle","display_name":"Superposition principle","score":0.553178608417511},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5448753833770752},{"id":"https://openalex.org/keywords/resistive-touchscreen","display_name":"Resistive touchscreen","score":0.48510539531707764},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.4732024371623993},{"id":"https://openalex.org/keywords/activation-function","display_name":"Activation function","score":0.4562079608440399},{"id":"https://openalex.org/keywords/learning-rule","display_name":"Learning rule","score":0.4109063446521759},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4066624641418457},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3803979158401489},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31102678179740906},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2608814537525177},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.20084208250045776},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.12310802936553955}],"concepts":[{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.6253525018692017},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5903246998786926},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5747908353805542},{"id":"https://openalex.org/C27753989","wikidata":"https://www.wikidata.org/wiki/Q284885","display_name":"Superposition principle","level":2,"score":0.553178608417511},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5448753833770752},{"id":"https://openalex.org/C6899612","wikidata":"https://www.wikidata.org/wiki/Q852911","display_name":"Resistive touchscreen","level":2,"score":0.48510539531707764},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.4732024371623993},{"id":"https://openalex.org/C38365724","wikidata":"https://www.wikidata.org/wiki/Q4677469","display_name":"Activation function","level":3,"score":0.4562079608440399},{"id":"https://openalex.org/C2779127903","wikidata":"https://www.wikidata.org/wiki/Q6510194","display_name":"Learning rule","level":3,"score":0.4109063446521759},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4066624641418457},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3803979158401489},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31102678179740906},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2608814537525177},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.20084208250045776},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.12310802936553955},{"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/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1162/neco_a_01648","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_01648","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718","https://openalex.org/P4310316440"],"host_organization_lineage_names":["The MIT Press","Massachusetts Institute of Technology"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},{"id":"pmid:38457749","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38457749","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural computation","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W2003858978","https://openalex.org/W2047984762","https://openalex.org/W2066188896","https://openalex.org/W2096629297","https://openalex.org/W2112246162","https://openalex.org/W2118337051","https://openalex.org/W2165432985","https://openalex.org/W2527798464","https://openalex.org/W2809466050","https://openalex.org/W3032896721","https://openalex.org/W3035249656","https://openalex.org/W3081134611","https://openalex.org/W3105568359","https://openalex.org/W3123648691","https://openalex.org/W3139064159","https://openalex.org/W3162006056","https://openalex.org/W3169104110","https://openalex.org/W3191589221","https://openalex.org/W4210678178","https://openalex.org/W4212774754","https://openalex.org/W4220714169","https://openalex.org/W4281859585","https://openalex.org/W4309760588","https://openalex.org/W4319654104","https://openalex.org/W4323825596","https://openalex.org/W4376491423","https://openalex.org/W4382059286","https://openalex.org/W4386621519","https://openalex.org/W6843269113","https://openalex.org/W6847691244"],"related_works":["https://openalex.org/W4308749380","https://openalex.org/W2037841693","https://openalex.org/W3110577345","https://openalex.org/W2372481764","https://openalex.org/W1977264433","https://openalex.org/W3016975909","https://openalex.org/W2132048588","https://openalex.org/W2605961050","https://openalex.org/W2945998886","https://openalex.org/W2146533867"],"abstract_inverted_index":{"We":[0,108,176],"introduce":[1],"frequency":[2,32,79,111],"propagation,":[3],"a":[4,12,25,40,105,117],"learning":[5,94,119,132,182],"algorithm":[6],"for":[7,121],"nonlinear":[8],"physical":[9,122,138,144],"networks.":[10,130],"In":[11],"resistive":[13],"electrical":[14],"circuit":[15,53],"with":[16],"variable":[17],"resistors,":[18],"an":[19,34,62,66,114],"activation":[20,63,149],"current":[21,36],"is":[22,37,58,83,96,113,164],"applied":[23,38],"at":[24,30,39,45,135],"set":[26,41],"of":[27,42,51,61,77,89,116,199],"input":[28],"nodes":[29,44],"one":[31],"and":[33,65,98,150],"error":[35,67,151],"output":[43],"another":[46],"frequency.":[47],"The":[48,93],"voltage":[49],"response":[50],"the":[52,59,78,87,90,154,169,197],"to":[54,86,100,146,167,172],"these":[55,161],"boundary":[56],"currents":[57],"superposition":[60],"signal":[64,68],"whose":[69],"coefficients":[70],"can":[71],"be":[72,124],"read":[73],"in":[74,153,186],"different":[75],"frequencies":[76],"domain.":[80],"Each":[81],"conductance":[82],"updated":[84],"proportionally":[85],"product":[88],"two":[91,137,162],"coefficients.":[92],"rule":[95],"local":[97],"proved":[99],"perform":[101,173],"gradient":[102,174],"descent":[103],"on":[104],"loss":[106],"function.":[107],"argue":[109],"that":[110],"propagation":[112],"instance":[115],"multimechanism":[118,200],"strategy":[120],"networks,":[123],"it":[125],"resistive,":[126],"elastic,":[127],"or":[128],"flow":[129,187],"Multimechanism":[131],"strategies":[133],"incorporate":[134],"least":[136],"quantities,":[139],"potentially":[140],"governed":[141],"by":[142],"independent":[143],"mechanisms,":[145],"act":[147],"as":[148],"signals":[152,163],"training":[155],"process.":[156],"Locally":[157],"available":[158],"information":[159],"about":[160],"then":[165],"used":[166],"update":[168],"trainable":[170],"parameters":[171],"descent.":[175],"demonstrate":[177],"how":[178],"earlier":[179],"work":[180],"implementing":[181],"via":[183],"chemical":[184],"signaling":[185],"networks":[188],"(Anisetti,":[189],"Scellier,":[190],"et":[191],"al.,":[192],"2023)":[193],"also":[194],"falls":[195],"under":[196],"rubric":[198],"learning.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6}],"updated_date":"2026-08-18T07:49:30.821534","created_date":"2025-10-10T00:00:00"}
