{"id":"https://openalex.org/W7133556034","doi":"https://doi.org/10.48550/arxiv.2603.02899","title":"Embedding interpretable $\\ell_1$-regression into neural networks for uncovering temporal structure in cell imaging","display_name":"Embedding interpretable $\\ell_1$-regression into neural networks for uncovering temporal structure in cell imaging","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7133556034","doi":"https://doi.org/10.48550/arxiv.2603.02899"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.02899","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128089887","display_name":"Fabian Kabus","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kabus, Fabian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060127864","display_name":"Maren Hackenberg","orcid":"https://orcid.org/0000-0003-4403-634X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hackenberg, Maren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128125493","display_name":"Julia Hindel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hindel, Julia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066820133","display_name":"Thibault Cholvin","orcid":"https://orcid.org/0000-0002-9964-7693"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cholvin, Thibault","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003842299","display_name":"Antje Kilias","orcid":"https://orcid.org/0000-0001-6691-7194"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kilias, Antje","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012022217","display_name":"Thomas Brox","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brox, Thomas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114637789","display_name":"Abhinav Valada","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Valada, Abhinav","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016035672","display_name":"Marlene Bartos","orcid":"https://orcid.org/0000-0001-9741-1946"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bartos, Marlene","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Binder, Harald","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Binder, Harald","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":0.22423109,"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/T10581","display_name":"Neural dynamics and brain function","score":0.21240000426769257,"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":0.21240000426769257,"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/T10977","display_name":"Optical Imaging and Spectroscopy Techniques","score":0.08619999885559082,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.07329999655485153,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6230000257492065},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.6122000217437744},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5974000096321106},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5723000168800354},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5479000210762024},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5120999813079834},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4645000100135803},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4611000120639801},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4154999852180481},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4004000127315521}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.704200029373169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6692000031471252},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6230000257492065},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6122000217437744},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5974000096321106},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5723000168800354},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5479000210762024},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5120999813079834},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4645000100135803},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4611000120639801},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4503999948501587},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4154999852180481},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.39239999651908875},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.3140999972820282},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.296099990606308},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.27630001306533813},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C17095337","wikidata":"https://www.wikidata.org/wiki/Q2375229","display_name":"Piecewise linear function","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.02899","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.02899","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02899","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.02899","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"artificial":[1],"neural":[2],"networks":[3],"excel":[4],"in":[5,18],"unsupervised":[6],"learning":[7],"of":[8,29,41,104],"non-sparse":[9,91],"structure,":[10],"classical":[11],"statistical":[12,135],"regression":[13,72,105],"techniques":[14],"offer":[15],"better":[16],"interpretability,":[17],"particular":[19],"when":[20],"sparseness":[21],"is":[22,107,118],"enforced":[23],"by":[24,109],"$\\ell_1$":[25],"regularization,":[26],"enabling":[27],"identification":[28],"which":[30,78,155],"factors":[31],"drive":[32,158],"observed":[33],"dynamics.":[34,161],"We":[35,61],"investigate":[36],"how":[37],"these":[38],"two":[39],"types":[40],"approaches":[42,121],"can":[43],"be":[44,59],"optimally":[45],"combined,":[46],"exemplarily":[47],"considering":[48],"two-photon":[49],"calcium":[50],"imaging":[51],"data":[52],"where":[53,122],"sparse":[54,97],"autoregressive":[55,66],"dynamics":[56],"are":[57],"to":[58,128],"extracted.":[60],"propose":[62],"embedding":[63],"a":[64,75,139],"vector":[65],"(VAR)":[67],"model":[68,136],"as":[69],"an":[70,133],"interpretable":[71],"technique":[73],"into":[74,99],"convolutional":[76],"autoencoder,":[77],"provides":[79],"dimension":[80],"reduction":[81],"for":[82,142],"tractable":[83],"temporal":[84,144],"modeling.":[85],"A":[86],"skip":[87],"connection":[88],"separately":[89],"addresses":[90],"static":[92],"spatial":[93,156],"information,":[94],"selectively":[95],"channeling":[96],"structure":[98],"the":[100,112,123,129,147,159],"$\\ell_1$-regularized":[101],"VAR.":[102],"$\\ell_1$-estimation":[103],"parameters":[106],"enabled":[108],"differentiating":[110],"through":[111],"piecewise":[113],"linear":[114],"solution":[115],"path.":[116],"This":[117],"contrasted":[119],"with":[120],"autoencoder":[124],"does":[125],"not":[126],"adapt":[127],"VAR":[130],"model.":[131],"Having":[132],"embedded":[134],"also":[137],"enables":[138],"testing":[140],"approach":[141],"comparing":[143],"sequences":[145],"from":[146],"same":[148],"observational":[149],"unit.":[150],"Additionally,":[151],"contribution":[152],"maps":[153],"visualize":[154],"regions":[157],"learned":[160]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-03-05T00:00:00"}
