{"id":"https://openalex.org/W4408345911","doi":"https://doi.org/10.1109/icassp49660.2025.10887614","title":"Schoenberg Kernel Loss for Spiking Neural Network Training","display_name":"Schoenberg Kernel Loss for Spiking Neural Network Training","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408345911","doi":"https://doi.org/10.1109/icassp49660.2025.10887614"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10887614","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10887614","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"article","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/A5114083721","display_name":"Liyang Ru","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":false,"raw_author_name":"Liyang Ru","raw_affiliation_strings":["University of Florida,Electrical and Computer Engineering,Gainesville,FL,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florida,Electrical and Computer Engineering,Gainesville,FL,USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100342150","display_name":"Kan Li","orcid":"https://orcid.org/0000-0002-1618-919X"},"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":false,"raw_author_name":"Kan Li","raw_affiliation_strings":["University of Florida,Electrical and Computer Engineering,Gainesville,FL,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florida,Electrical and Computer Engineering,Gainesville,FL,USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019504861","display_name":"Jos\u00e9 C. Pr\u0131\u0301ncipe","orcid":"https://orcid.org/0000-0002-3449-3531"},"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":false,"raw_author_name":"Jos\u00e9 C. Pr\u00edncipe","raw_affiliation_strings":["University of Florida,Electrical and Computer Engineering,Gainesville,FL,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florida,Electrical and Computer Engineering,Gainesville,FL,USA","institution_ids":["https://openalex.org/I33213144"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I33213144"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01618444,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9980000257492065,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9980000257492065,"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":0.9972000122070312,"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":0.9850000143051147,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6908852458000183},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.6460128426551819},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6402928233146667},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5951242446899414},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5211805701255798},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49190592765808105},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34471485018730164},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08466258645057678}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6908852458000183},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.6460128426551819},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6402928233146667},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5951242446899414},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5211805701255798},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49190592765808105},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34471485018730164},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08466258645057678},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10887614","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10887614","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1512200437","https://openalex.org/W1645800954","https://openalex.org/W2016574277","https://openalex.org/W2114155871","https://openalex.org/W2171445446","https://openalex.org/W2314470091","https://openalex.org/W2569813014","https://openalex.org/W2735633774","https://openalex.org/W2745933219","https://openalex.org/W2754486806","https://openalex.org/W2775079417","https://openalex.org/W2795505726","https://openalex.org/W2892077605","https://openalex.org/W2962804204","https://openalex.org/W2963743287","https://openalex.org/W2984844508","https://openalex.org/W3007283957","https://openalex.org/W3038819247","https://openalex.org/W3102040318","https://openalex.org/W3120498123","https://openalex.org/W3184737150","https://openalex.org/W3198290099","https://openalex.org/W4312741181","https://openalex.org/W4385484543","https://openalex.org/W6752051825","https://openalex.org/W6754595668","https://openalex.org/W6779514680","https://openalex.org/W6809648081"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3126544799","https://openalex.org/W3216976533","https://openalex.org/W100620283","https://openalex.org/W2495260952","https://openalex.org/W3104333581"],"abstract_inverted_index":{"In":[0,68],"this":[1],"paper,":[2],"we":[3,70],"propose":[4],"the":[5,14,27,48,85],"Schoenberg":[6],"kernel":[7],"loss,":[8],"a":[9],"loss":[10,62],"function":[11],"that":[12],"measures":[13],"divergence":[15],"between":[16],"spike":[17,98],"trains":[18],"for":[19],"spiking":[20,31],"neural":[21],"network":[22,66],"training.":[23],"Our":[24],"method":[25,58],"addresses":[26],"challenges":[28],"of":[29,87],"non-differentiable":[30],"neurons":[32],"and":[33,37,53,101],"leverages":[34],"both":[35],"spatial":[36],"temporal":[38],"dynamics":[39],"in":[40,78,104],"information":[41],"processing.":[42],"We":[43],"evaluated":[44],"our":[45,57,65,72],"approach":[46],"on":[47,64,96],"neuromorphic":[49,105],"benchmark":[50],"datasets,":[51],"N-MNIST,":[52],"DVS":[54],"Gesture,":[55],"demonstrating":[56],"outperforms":[59],"previously":[60],"published":[61],"functions":[63],"architecture.":[67],"addition,":[69],"extend":[71],"investigation":[73],"to":[74,84],"real-time":[75],"classification":[76],"simulations":[77],"real-world":[79],"scenarios.":[80],"This":[81],"work":[82],"contributes":[83],"advancement":[86],"direct":[88],"SNN":[89],"training":[90],"methods,":[91],"offering":[92],"improved":[93],"performance,":[94],"particularly":[95],"low":[97],"density":[99],"data,":[100],"potential":[102],"applications":[103],"classification.":[106]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
