{"id":"https://openalex.org/W7216010990","doi":"https://doi.org/10.48550/arxiv.2610.01786","title":"Inferring Multi-Timescale Neural Dynamics with Switching Linear Dynamical Systems","display_name":"Inferring Multi-Timescale Neural Dynamics with Switching Linear Dynamical Systems","publication_year":2026,"publication_date":"2026-10-01","ids":{"openalex":"https://openalex.org/W7216010990","doi":"https://doi.org/10.48550/arxiv.2610.01786"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.01786","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.01786","pdf_url":"https://arxiv.org/pdf/2610.01786","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.01786","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5155713489","display_name":"Lulu Gong","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gong, Lulu","raw_affiliation_strings":["Center for Neurocomputation and Machine Intelligence , Wu Tsai Institute","Department of Biomedical Engineering Yale University New Haven , CT 06510 , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Neurocomputation and Machine Intelligence , Wu Tsai Institute","institution_ids":[]},{"raw_affiliation_string":"Department of Biomedical Engineering Yale University New Haven , CT 06510 , USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065745151","display_name":"Yongxu Zhang","orcid":"https://orcid.org/0009-0008-3251-4248","observed_orcids":["https://orcid.org/0009-0008-3251-4248"]},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhang, Yongxu","raw_affiliation_strings":["Center for Neurocomputation and Machine Intelligence , Wu Tsai Institute","Department of Biomedical Engineering Yale University New Haven , CT 06510 , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Neurocomputation and Machine Intelligence , Wu Tsai Institute","institution_ids":[]},{"raw_affiliation_string":"Department of Biomedical Engineering Yale University New Haven , CT 06510 , USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022362167","display_name":"Shreya Saxena","orcid":"https://orcid.org/0000-0003-4655-7050","observed_orcids":["https://orcid.org/0000-0003-4655-7050"]},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saxena, Shreya","raw_affiliation_strings":["Center for Neurocomputation and Machine Intelligence , Wu Tsai Institute","Department of Biomedical Engineering Yale University New Haven , CT 06510 , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Neurocomputation and Machine Intelligence , Wu Tsai Institute","institution_ids":[]},{"raw_affiliation_string":"Department of Biomedical Engineering Yale University New Haven , CT 06510 , USA","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I32971472"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"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/T10581","display_name":"Neural dynamics and brain function","score":0.948199987411499,"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"}},"topics":[{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.948199987411499,"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/T11236","display_name":"Control Systems and Identification","score":0.02239999920129776,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.00839999970048666,"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/switching-linear-dynamical-systems","display_name":"switching linear dynamical systems","score":0.9890000224113464},{"id":"https://openalex.org/keywords/regime-switching-models","display_name":"regime switching","score":0.5640000104904175},{"id":"https://openalex.org/keywords/state-space-models","display_name":"state-space model","score":0.7279999852180481},{"id":"https://openalex.org/keywords/transition-matrix-eigenvalues","display_name":"transition matrix eigenvalues","score":0.6759999990463257},{"id":"https://openalex.org/keywords/neural-population-activity","display_name":"neural population activity","score":0.7310000061988831}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5501000285148621},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5066999793052673},{"id":"https://openalex.org/C79379906","wikidata":"https://www.wikidata.org/wiki/Q3174497","display_name":"Dynamical systems theory","level":2,"score":0.48890000581741333},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.450300008058548},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.44510000944137573},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44359999895095825},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.43320000171661377},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4235000014305115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4187000095844269},{"id":"https://openalex.org/C33962884","wikidata":"https://www.wikidata.org/wiki/Q378637","display_name":"Dynamical system (definition)","level":3,"score":0.40790000557899475},{"id":"https://openalex.org/C114275822","wikidata":"https://www.wikidata.org/wiki/Q621512","display_name":"Linear dynamical system","level":3,"score":0.3970000147819519},{"id":"https://openalex.org/C2909946758","wikidata":"https://www.wikidata.org/wiki/Q194277","display_name":"Spike train","level":3,"score":0.39160001277923584},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.35600000619888306},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35109999775886536},{"id":"https://openalex.org/C3832189","wikidata":"https://www.wikidata.org/wiki/Q8588916","display_name":"Models of neural computation","level":3,"score":0.33739998936653137},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3343000113964081},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3337000012397766},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.296999990940094},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C77405623","wikidata":"https://www.wikidata.org/wiki/Q598451","display_name":"System dynamics","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28459998965263367},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2831999957561493},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25189998745918274},{"id":"https://openalex.org/C55689738","wikidata":"https://www.wikidata.org/wiki/Q15963867","display_name":"Discrete time and continuous time","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.01786","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.01786","pdf_url":"https://arxiv.org/pdf/2610.01786","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.01786","doi":"https://doi.org/10.48550/arxiv.2610.01786","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.01786","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.01786","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.01786","pdf_url":"https://arxiv.org/pdf/2610.01786","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7216010990.pdf","grobid_xml":"https://content.openalex.org/works/W7216010990.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2918998317","https://openalex.org/W4306827181","https://openalex.org/W7140183742","https://openalex.org/W6945612242","https://openalex.org/W4386215530","https://openalex.org/W4407024903","https://openalex.org/W4407201086","https://openalex.org/W3099240049","https://openalex.org/W2545581354","https://openalex.org/W4377089324"],"abstract_inverted_index":{"Neural":[0],"activity":[1,67],"often":[2],"exhibits":[3],"multiple":[4,83,134,185],"timescales":[5,17,84,116,151,180],"that":[6],"can":[7,46,152],"vary":[8],"with":[9,53,137,171],"behavioral":[10],"states":[11],"and":[12,28,45,75,85,168,173,181],"task":[13],"conditions.":[14],"Identifying":[15],"these":[16,99],"from":[18,117,157],"neural":[19,26,50,65,121,169],"recordings":[20,44],"is":[21],"important":[22],"for":[23,62,82,112],"better":[24],"understanding":[25],"computation":[27],"function.":[29],"However,":[30],"traditional":[31],"approaches":[32],"based":[33],"on":[34],"autocorrelation":[35],"fitting":[36],"are":[37],"difficult":[38],"to":[39,41],"scale":[40],"high-dimensional":[42,64],"population":[43,66],"become":[47],"unreliable":[48],"when":[49],"dynamics":[51],"change":[52],"behavior.":[54],"State-space":[55],"models":[56],"have":[57],"been":[58],"a":[59,110,125],"powerful":[60],"framework":[61,111],"modeling":[63],"through":[68],"latent":[69,115,163],"dynamical":[70,145],"systems,":[71],"but":[72],"standard":[73],"formulations":[74],"inference":[76],"methods":[77],"do":[78,87],"not":[79,88],"explicitly":[80],"account":[81],"therefore":[86],"guarantee":[89],"accurate":[90],"recovery":[91],"of":[92,144,160],"the":[93,103,158,161],"underlying":[94],"temporal":[95,131],"structure.":[96],"Motivated":[97],"by":[98],"questions,":[100],"we":[101],"introduce":[102],"Multi-Timescale":[104],"Switching":[105],"Linear":[106],"Dynamical":[107],"System":[108],"(MTS-SLDS),":[109],"identifying":[113],"regime-specific":[114],"continuous":[118],"or":[119],"spiking":[120],"observations.":[122],"MTS-SLDS":[123,177],"combines":[124],"multi-lag":[126],"moment":[127],"initialization,":[128],"which":[129,141],"captures":[130],"structure":[132,183],"across":[133,147],"observation":[135],"lags,":[136],"\\textit{regime-conditioned}":[138],"Laplace-EM":[139],"inference,":[140],"reduces":[142],"mixing":[143],"statistics":[146],"uncertain":[148],"regimes.":[149],"Characteristic":[150],"then":[153],"be":[154],"extracted":[155],"directly":[156],"eigenvalues":[159],"learned":[162],"transition":[164],"matrices.":[165],"In":[166],"synthetic":[167],"experiments":[170],"Gaussian":[172],"Poisson":[174],"spike":[175],"observations,":[176],"accurately":[178],"recovers":[179],"switching":[182],"over":[184],"datasets.":[186]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2026-10-03T00:00:00"}
