{"id":"https://openalex.org/W7213400970","doi":"https://doi.org/10.48550/arxiv.2609.17213","title":"Latent kinetic Ising models of neural spike trains","display_name":"Latent kinetic Ising models of neural spike trains","publication_year":2026,"publication_date":"2026-09-15","ids":{"openalex":"https://openalex.org/W7213400970","doi":"https://doi.org/10.48550/arxiv.2609.17213"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.17213","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.17213","pdf_url":"https://arxiv.org/pdf/2609.17213","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/2609.17213","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070869980","display_name":"Davide Ghio","orcid":"https://orcid.org/0000-0002-9799-5264","observed_orcids":["https://orcid.org/0000-0002-9799-5264"]},"institutions":[{"id":"https://openalex.org/I169199633","display_name":"Aston University","ror":"https://ror.org/05j0ve876","country_code":"GB","type":"education","lineage":["https://openalex.org/I169199633"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ghio, Davide","raw_affiliation_strings":["College of Engineering and Physical Sciences , Aston University , Birmingham , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Engineering and Physical Sciences , Aston University , Birmingham , UK","institution_ids":["https://openalex.org/I169199633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081568136","display_name":"David Saad","orcid":"https://orcid.org/0000-0001-9821-2623","observed_orcids":["https://orcid.org/0000-0001-9821-2623"]},"institutions":[{"id":"https://openalex.org/I169199633","display_name":"Aston University","ror":"https://ror.org/05j0ve876","country_code":"GB","type":"education","lineage":["https://openalex.org/I169199633"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Saad, David","raw_affiliation_strings":["College of Engineering and Physical Sciences , Aston University , Birmingham , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Engineering and Physical Sciences , Aston University , Birmingham , UK","institution_ids":["https://openalex.org/I169199633"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I169199633"],"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":0.9854000210762024,"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":0.9854000210762024,"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/T11513","display_name":"stochastic dynamics and bifurcation","score":0.0015999999595806003,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"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/T11830","display_name":"Point processes and geometric inequalities","score":0.0010999999940395355,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/kinetic-ising-model","display_name":"kinetic Ising model","score":0.8999999761581421},{"id":"https://openalex.org/keywords/spike-trains","display_name":"spike trains","score":0.9070000052452087},{"id":"https://openalex.org/keywords/effective-connectivity","display_name":"effective connectivity","score":0.47099998593330383},{"id":"https://openalex.org/keywords/latent-variable-modeling","display_name":"latent variable models","score":0.7609999775886536},{"id":"https://openalex.org/keywords/point-processes","display_name":"point processes","score":0.8569999933242798},{"id":"https://openalex.org/keywords/refractoriness","display_name":"refractoriness","score":0.8930000066757202},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"maximum a posteriori estimation","score":0.7549999952316284},{"id":"https://openalex.org/keywords/sparse-network-structure","display_name":"sparse network structure","score":0.6729999780654907},{"id":"https://openalex.org/keywords/dale-s-law","display_name":"Dale's law","score":0.9430000185966492}],"concepts":[{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.6370000243186951},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6021000146865845},{"id":"https://openalex.org/C51329190","wikidata":"https://www.wikidata.org/wiki/Q1076349","display_name":"Ising model","level":2,"score":0.5992000102996826},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5684999823570251},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.4440999925136566},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44209998846054077},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4348999857902527},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42989999055862427},{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.42660000920295715},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4007999897003174},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.3837999999523163},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3619000017642975},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C2909946758","wikidata":"https://www.wikidata.org/wiki/Q194277","display_name":"Spike train","level":3,"score":0.3424000144004822},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C13355873","wikidata":"https://www.wikidata.org/wiki/Q2920850","display_name":"Connection (principal bundle)","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C123757187","wikidata":"https://www.wikidata.org/wiki/Q9195957","display_name":"Network dynamics","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.3174000084400177},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.27000001072883606},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2680000066757202},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C33010914","wikidata":"https://www.wikidata.org/wiki/Q7291980","display_name":"Random neural network","level":4,"score":0.2590000033378601},{"id":"https://openalex.org/C55689738","wikidata":"https://www.wikidata.org/wiki/Q15963867","display_name":"Discrete time and continuous time","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.25189998745918274},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2506999969482422}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.17213","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.17213","pdf_url":"https://arxiv.org/pdf/2609.17213","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.2609.17213","doi":"https://doi.org/10.48550/arxiv.2609.17213","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.17213","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.17213","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.17213","pdf_url":"https://arxiv.org/pdf/2609.17213","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/W7213400970.pdf","grobid_xml":"https://content.openalex.org/works/W7213400970.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W4302548634"],"related_works":["https://openalex.org/W4302548634","https://openalex.org/W374309463","https://openalex.org/W4301154516","https://openalex.org/W1972854592","https://openalex.org/W1996326643","https://openalex.org/W2049697604","https://openalex.org/W7100400854","https://openalex.org/W7100752648","https://openalex.org/W2122577351","https://openalex.org/W7083178347"],"abstract_inverted_index":{"Inferring":[0],"directed":[1],"effective":[2,115],"interactions":[3],"from":[4],"neuronal":[5],"spike":[6,38,162],"trains":[7,39,163],"is":[8],"a":[9,23,59,102,142,166],"central":[10],"inverse":[11],"problem":[12],"in":[13,107],"statistical":[14],"physics":[15],"and":[16,47,97,126,141,157,180,185,196],"computational":[17],"neuroscience.":[18],"Kinetic":[19],"Ising":[20,73],"models":[21],"provide":[22],"tractable":[24],"framework":[25,130],"for":[26,95],"this":[27,173],"task,":[28],"but":[29],"their":[30],"application":[31],"to":[32,133],"neural":[33],"data":[34,156],"typically":[35],"requires":[36],"binning":[37],"into":[40],"binary":[41],"activity":[42],"variables,":[43],"discarding":[44],"within-bin":[45],"timing":[46],"conflating":[48],"collective":[49,76],"network":[50,77,124],"dynamics":[51,195],"with":[52,136],"single-neuron":[53],"history":[54],"effects.":[55],"We":[56,100,149],"introduce":[57],"SpiKIsing,":[58],"latent-variable":[60],"model":[61,74],"that":[62],"separates":[63],"these":[64],"two":[65],"levels":[66],"of":[67,121],"description.":[68],"A":[69],"discrete-time":[70],"asymmetric":[71],"kinetic":[72],"describes":[75],"activity,":[78,123],"while":[79],"continuous-time,":[80],"history-dependent":[81],"point":[82],"processes":[83],"generate":[84],"the":[85,90,109,159,189,193,197],"observed":[86],"spikes":[87],"conditional":[88],"on":[89,153,161],"latent":[91,122],"states,":[92],"accounting":[93],"explicitly":[94],"refractoriness":[96],"post-spike":[98],"recovery.":[99],"derive":[101],"variational":[103],"mean-field":[104],"expectation-maximization":[105],"scheme":[106],"which":[108],"point-process":[110],"likelihood":[111],"enters":[112],"as":[113],"an":[114],"observation":[116],"field,":[117],"enabling":[118],"joint":[119],"inference":[120,135],"couplings,":[125],"emission":[127],"parameters.":[128],"The":[129],"extends":[131],"naturally":[132],"maximum-a-posteriori":[134],"structured":[137],"priors,":[138],"including":[139],"sparsity":[140],"hierarchical":[143],"extension":[144],"favouring":[145],"Dale-consistent":[146],"outgoing":[147],"interactions.":[148],"validate":[150],"parameter":[151],"recovery":[152],"matched":[154],"synthetic":[155],"test":[158],"method":[160],"generated":[164],"by":[165],"recurrent":[167],"conductance-based":[168],"leaky":[169],"integrate-and-fire":[170],"network.":[171],"In":[172],"setting,":[174],"SpiKIsing":[175,198],"recovers":[176],"sparse":[177],"connection":[178],"structure":[179],"correctly":[181],"classifies":[182],"all":[183],"excitatory":[184],"inhibitory":[186],"neurons":[187],"despite":[188],"substantial":[190],"mismatch":[191],"between":[192],"data-generating":[194],"model.":[199]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-17T00:00:00"}
