{"id":"https://openalex.org/W3046551973","doi":"https://doi.org/10.7916/d8-79ec-r948","title":"Statistical Machine Learning & Deep Neural Networks Applied to Neural Data Analysis","display_name":"Statistical Machine Learning & Deep Neural Networks Applied to Neural Data Analysis","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3046551973","doi":"https://doi.org/10.7916/d8-79ec-r948","mag":"3046551973"},"language":"en","primary_location":{"id":"mag:3046551973","is_oa":false,"landing_page_url":"https://academiccommons.columbia.edu/doi/10.7916/d8-79ec-r948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.7916/d8-79ec-r948","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075851713","display_name":"Hooshmand Shokri Razaghi","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shokri Razaghi, Hooshmand","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5075851713"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08715653,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.19210000336170197,"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":0.19210000336170197,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.7794854640960693},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.629300594329834},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6044574975967407},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.525365948677063},{"id":"https://openalex.org/keywords/types-of-artificial-neural-networks","display_name":"Types of artificial neural networks","score":0.4908748269081116},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.469255656003952},{"id":"https://openalex.org/keywords/statistical-learning","display_name":"Statistical learning","score":0.42628708481788635},{"id":"https://openalex.org/keywords/time-delay-neural-network","display_name":"Time delay neural network","score":0.28516700863838196}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7794854640960693},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.629300594329834},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6044574975967407},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.525365948677063},{"id":"https://openalex.org/C177973122","wikidata":"https://www.wikidata.org/wiki/Q7860946","display_name":"Types of artificial neural networks","level":4,"score":0.4908748269081116},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.469255656003952},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.42628708481788635},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.28516700863838196}],"mesh":[],"locations_count":2,"locations":[{"id":"mag:3046551973","is_oa":false,"landing_page_url":"https://academiccommons.columbia.edu/doi/10.7916/d8-79ec-r948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null},{"id":"doi:10.7916/d8-79ec-r948","is_oa":true,"landing_page_url":"https://doi.org/10.7916/d8-79ec-r948","pdf_url":null,"source":{"id":"https://openalex.org/S4306402601","display_name":"Columbia Academic Commons (Columbia University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I78577930","host_organization_name":"Columbia University","host_organization_lineage":["https://openalex.org/I78577930"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.7916/d8-79ec-r948","is_oa":true,"landing_page_url":"https://doi.org/10.7916/d8-79ec-r948","pdf_url":null,"source":{"id":"https://openalex.org/S4306402601","display_name":"Columbia Academic Commons (Columbia University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I78577930","host_organization_name":"Columbia University","host_organization_lineage":["https://openalex.org/I78577930"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4014406470","display_name":null,"funder_award_id":"-17-C-","funder_id":"https://openalex.org/F4320306078","funder_display_name":"U.S. Department of Defense"},{"id":"https://openalex.org/G4161800100","display_name":null,"funder_award_id":"N66001-17-C-4002","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G545205837","display_name":"CRCNS: Collaborative Research: Naturalistic computation and signaling by neural populations in the primate retina","funder_award_id":"1430239","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6247393980","display_name":null,"funder_award_id":"1546296","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306078","display_name":"U.S. Department of Defense","ror":"https://ror.org/0447fe631"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W384164522","https://openalex.org/W45083433","https://openalex.org/W3013106689","https://openalex.org/W2163346240","https://openalex.org/W3194581889","https://openalex.org/W172661617","https://openalex.org/W2795319120","https://openalex.org/W3041591209","https://openalex.org/W3097464668","https://openalex.org/W3196981052","https://openalex.org/W2589419925","https://openalex.org/W2317434326","https://openalex.org/W2140593367","https://openalex.org/W1223313510","https://openalex.org/W1606551523","https://openalex.org/W3033017292","https://openalex.org/W1994896101","https://openalex.org/W2889117570","https://openalex.org/W180587397","https://openalex.org/W2051220667"],"abstract_inverted_index":{"Computational":[0],"neuroscience":[1,206],"seeks":[2],"to":[3,82,119,139,160,223,243,274,281,288,332,350,380,422],"discover":[4],"the":[5,15,23,29,73,87,131,177,293,301,334,343,358,413],"underlying":[6,77],"mechanisms":[7],"by":[8,255],"which":[9],"neural":[10,19,35,110,374],"activity":[11],"is":[12,28,113,117,199,213,252],"generated.":[13],"With":[14],"recent":[16,102],"advancement":[17],"in":[18,38,101,123,173,312,339,399,447],"data":[20,36,111,198,212,290],"acquisition":[21],"methods,":[22],"bottleneck":[24],"of":[25,31,34,55,86,135,143,182,193,205,211,337,373,426,449,451,455],"this":[26,91,146,183,209,282],"pursuit":[27],"analysis":[30,112,192],"ever-growing":[32],"volume":[33],"acquired":[37],"numerous":[39],"labs":[40],"from":[41,60,217,247,292],"various":[42],"experiments.":[43],"These":[44],"analyses":[45],"can":[46,419],"be":[47,224,420],"broadly":[48],"divided":[49],"into":[50,226],"two":[51,389],"categories.":[52],"First,":[53],"extraction":[54],"high":[56],"quality":[57,454],"neuronal":[58,74],"signals":[59,75,251],"noisy":[61],"large":[62],"scale":[63],"recordings.":[64,239,341],"Second,":[65],"inference":[66,165,351,382,398,410,445],"for":[67,90,109,166,201,236,296,352,383,396],"statistical":[68,153],"models":[69,428],"aimed":[70],"at":[71],"explaining":[72],"and":[76,97,126,130,150,154,164,179,203,219,263,304,325,366,453,458],"processes":[78],"that":[79,169,278,308,418],"give":[80],"rise":[81],"them.":[83],"Conventionally,":[84],"majority":[85],"methodologies":[88],"employed":[89],"effort":[92],"are":[93,233,279,357],"based":[94,392],"on":[95,187,393,460],"statistics":[96],"signal":[98],"processing.":[99],"However,":[100,208],"years":[103],"recruiting":[104],"Artificial":[105],"Neural":[106,322],"Networks":[107],"(ANN)":[108],"gaining":[114],"traction.":[115],"This":[116],"due":[118],"their":[120],"immense":[121],"success":[122],"computer":[124],"vision":[125],"natural":[127],"language":[128],"processing,":[129],"stellar":[132],"track":[133],"record":[134],"ANN":[136,155],"architectures":[137,376],"generalizing":[138],"a":[140,261,321,371,423],"wide":[141,424],"variety":[142],"problems.":[144],"In":[145,176,342],"work":[147],"we":[148,185,346],"investigate":[149],"improve":[151],"upon":[152],"machine":[156],"learning":[157],"methods":[158,235,391,446],"applied":[159,421],"multi-electrode":[161,230],"array":[162],"recordings":[163,218],"dynamical":[167,385,402],"systems":[168,356],"play":[170],"critical":[171],"roles":[172],"computational":[174],"neuroscience.":[175],"first":[178],"second":[180],"part":[181],"thesis,":[184],"focus":[186],"spike":[188,196,227,245,266,315],"sorting":[189,267,316],"problem.":[190],"The":[191,240],"large-scale":[194],"multi-neuronal":[195],"train":[197],"crucial":[200],"current":[202],"future":[204],"research.":[207],"type":[210],"not":[214],"available":[215,305],"directly":[216],"require":[220],"further":[221],"processing":[222,241],"converted":[225],"trains.":[228],"Dense":[229],"arrays":[231],"(MEA)":[232],"standard":[234],"collecting":[237],"such":[238,299],"needed":[242],"extract":[244],"trains":[246],"these":[248],"raw":[249],"electrical":[250],"carried":[253],"out":[254],"``spike":[256],"sorting''":[257],"algorithms.":[258],"We":[259,284,318,387,404,432],"introduce":[260,320,388],"robust":[262],"scalable":[264],"MEA":[265,289,340],"pipeline":[268],"YASS":[269],"(Yet":[270],"Another":[271],"Spike":[272],"Sorter)":[273],"address":[275,333,381],"many":[276,362],"challenges":[277,303],"inherent":[280],"task.":[283],"primarily":[285],"pay":[286],"attention":[287,349],"collected":[291],"primate":[294],"retina":[295],"important":[297],"reasons":[298],"as":[300],"unique":[302],"side":[306],"information":[307],"ultimately":[309],"assist":[310],"us":[311],"scoring":[313],"different":[314,390],"pipelines.":[317],"also":[319,405],"Network":[323],"architecture":[324],"an":[326],"accompanying":[327],"training":[328],"scheme":[329],"specifically":[330],"devised":[331],"challenging":[335],"task":[336],"deconvolution":[338],"last":[344],"part,":[345],"shift":[347],"our":[348,434],"non-linear":[353,401],"dynamics.":[354,431],"Dynamical":[355],"governing":[359],"force":[360],"behind":[361],"real":[363],"world":[364],"phenomena":[365],"temporally":[367],"correlated":[368],"data.":[369,462],"Recently,":[370],"number":[372],"network":[375],"have":[377],"been":[378],"proposed":[379],"nonlinear":[384,430],"systems.":[386,403],"normalizing":[394],"flows":[395],"posterior":[397,409],"latent":[400],"present":[406],"gradient-based":[407],"amortized":[408],"approaches":[411],"using":[412],"auto-encoding":[414],"variational":[415],"Bayes":[416],"framework":[417],"range":[425],"generative":[427],"with":[429],"call":[433],"method":[435],"\ud835\ude0d\ud835\ude2a\ud835\ude2d\ud835\ude35\ud835\ude26\ud835\ude33\ud835\ude2a\ud835\ude2f\ud835\ude28":[436],"\ud835\ude15\ud835\ude30\ud835\ude33\ud835\ude2e\ud835\ude22\ud835\ude2d\ud835\ude2a\ud835\ude3b\ud835\ude2a\ud835\ude2f\ud835\ude28":[437],"\ud835\ude0d\ud835\ude2d\ud835\ude30\ud835\ude38\ud835\ude34":[438],"(FNF).":[439],"FNF":[440],"performs":[441],"favorably":[442],"against":[443],"state-of-the-art":[444],"terms":[448],"accuracy":[450],"predictions":[452],"uncovered":[456],"codes":[457],"dynamics":[459],"synthetic":[461]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2020-08-07T00:00:00"}
