{"id":"https://openalex.org/W7203486893","doi":"https://doi.org/10.48550/arxiv.2608.12388","title":"A Bayes-Markov Neuromorphic Model of Cortical Orientation Selectivity: A Computational Re-implementation and Quantitative Simulation Study","display_name":"A Bayes-Markov Neuromorphic Model of Cortical Orientation Selectivity: A Computational Re-implementation and Quantitative Simulation Study","publication_year":2026,"publication_date":"2026-08-01","ids":{"openalex":"https://openalex.org/W7203486893","doi":"https://doi.org/10.48550/arxiv.2608.12388"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.12388","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.12388","pdf_url":"https://arxiv.org/pdf/2608.12388","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/2608.12388","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026028670","display_name":"Abolfazl Moslemi","orcid":"https://orcid.org/0009-0005-9194-3729","observed_orcids":["https://orcid.org/0009-0005-9194-3729"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moslemi, Abolfazl","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5147208714","display_name":"Milad Sarabadani","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sarabadani, Milad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5147202282","display_name":"Fatemeh Sefidian","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sefidian, Fatemeh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130975783","display_name":"Hossein Peyvandi","orcid":"https://orcid.org/0000-0001-7773-8351","observed_orcids":["https://orcid.org/0000-0001-7773-8351"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peyvandi, Hossein","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":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/T10427","display_name":"Visual perception and processing mechanisms","score":0.9794999957084656,"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/T10427","display_name":"Visual perception and processing mechanisms","score":0.9794999957084656,"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/T10581","display_name":"Neural dynamics and brain function","score":0.01269999984651804,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.0005000000237487257,"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/orientation-selectivity","display_name":"orientation selectivity","score":0.9340000152587891},{"id":"https://openalex.org/keywords/primary-visual-cortex","display_name":"primary visual cortex","score":0.9829999804496765},{"id":"https://openalex.org/keywords/lateral-geniculate-nucleus","display_name":"lateral geniculate nucleus","score":0.9660000205039978},{"id":"https://openalex.org/keywords/markov-random-fields","display_name":"Markov random fields","score":0.7979999780654907},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"maximum a posteriori estimation","score":0.8500000238418579},{"id":"https://openalex.org/keywords/orientation-tuning-curves","display_name":"orientation tuning curves","score":0.7139999866485596},{"id":"https://openalex.org/keywords/spiking-neuron-models","display_name":"spiking neuron models","score":0.6769999861717224},{"id":"https://openalex.org/keywords/leaky-integrate-and-fire-neurons","display_name":"leaky integrate-and-fire neurons","score":0.9290000200271606},{"id":"https://openalex.org/keywords/hodgkin-huxley-neurons","display_name":"Hodgkin-Huxley neurons","score":0.9879999756813049}],"concepts":[{"id":"https://openalex.org/C2776362945","wikidata":"https://www.wikidata.org/wiki/Q719179","display_name":"Lateral geniculate nucleus","level":3,"score":0.8003000020980835},{"id":"https://openalex.org/C2779345533","wikidata":"https://www.wikidata.org/wiki/Q75785","display_name":"Visual cortex","level":2,"score":0.6654000282287598},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6225000023841858},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5462999939918518},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.5202999711036682},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.46050000190734863},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4431999921798706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4203999936580658},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.4108999967575073},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4000999927520752},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.33160001039505005},{"id":"https://openalex.org/C556910895","wikidata":"https://www.wikidata.org/wiki/Q384188","display_name":"Magnetoencephalography","level":3,"score":0.3276999890804291},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C2781089630","wikidata":"https://www.wikidata.org/wiki/Q21856745","display_name":"Realization (probability)","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.3034999966621399},{"id":"https://openalex.org/C19071747","wikidata":"https://www.wikidata.org/wiki/Q1755207","display_name":"Receptive field","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.2736000120639801},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.26420000195503235},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2623000144958496},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C15286952","wikidata":"https://www.wikidata.org/wiki/Q8037925","display_name":"Computational neuroscience","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.25609999895095825},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.25380000472068787}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.12388","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.12388","pdf_url":"https://arxiv.org/pdf/2608.12388","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.2608.12388","doi":"https://doi.org/10.48550/arxiv.2608.12388","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.12388","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:2608.12388","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.12388","pdf_url":"https://arxiv.org/pdf/2608.12388","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":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7203486893.pdf","grobid_xml":"https://content.openalex.org/works/W7203486893.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1600534154","https://openalex.org/W1860783201","https://openalex.org/W1867287134","https://openalex.org/W1974658238","https://openalex.org/W1985940938","https://openalex.org/W1988013646","https://openalex.org/W1995181446","https://openalex.org/W2006546731","https://openalex.org/W2008240154","https://openalex.org/W2020999234","https://openalex.org/W2069973359","https://openalex.org/W2083309155","https://openalex.org/W2114220616","https://openalex.org/W2116279703","https://openalex.org/W2119885245","https://openalex.org/W2125663122","https://openalex.org/W2126020484","https://openalex.org/W2137413309","https://openalex.org/W2159749299","https://openalex.org/W2160008411","https://openalex.org/W2423540713","https://openalex.org/W2964259887","https://openalex.org/W3103106823"],"related_works":["https://openalex.org/W2069973359","https://openalex.org/W2165115754","https://openalex.org/W2056900767","https://openalex.org/W1982887525","https://openalex.org/W177640116","https://openalex.org/W2051551602","https://openalex.org/W7096348593","https://openalex.org/W2133861842","https://openalex.org/W316863127","https://openalex.org/W2013574444"],"abstract_inverted_index":{"The":[0,179],"emergence":[1],"of":[2,47,96,186,205],"orientation":[3,134,138,191],"selectivity":[4,139],"in":[5,15],"the":[6,44,56,75,101,118,131,167,182,187,206],"primary":[7],"visual":[8],"cortex":[9],"(V1)":[10],"remains":[11],"a":[12,22,61,63,68,81,88,112,154,199],"central":[13,183],"question":[14],"computational":[16],"neuroscience.":[17],"Shirazi's":[18],"Bayes-Markov":[19,188],"model":[20,132],"proposed":[21],"probabilistic":[23],"explanation":[24],"for":[25],"how":[26],"orientation-selective":[27],"inhibition":[28],"can":[29,171],"arise":[30],"from":[31,55],"non-oriented":[32],"lateral":[33],"geniculate":[34],"nucleus":[35],"(LGN)":[36],"inputs":[37],"through":[38,80,174],"local":[39,82,120],"inference.":[40],"In":[41],"that":[42,116],"formulation,":[43],"activity":[45,58],"pattern":[46,59],"striate":[48],"cortical":[49],"inhibitory":[50,208],"(SCI)":[51],"cells":[52],"is":[53,78],"estimated":[54],"LGN":[57,143,196],"by":[60],"maximum":[62],"posteriori":[64],"(MAP)":[65],"criterion":[66],"over":[67],"two-layer":[69],"hierarchical":[70],"Markov":[71],"random":[72],"field,":[73],"and":[74,92,110,148,161,198],"resulting":[76],"inference":[77],"implemented":[79],"parallel":[83],"relaxation":[84],"algorithm.":[85],"We":[86,99,129,151],"provide":[87],"computationally":[89],"explicit":[90,176],"re-implementation":[91],"quantitative":[93],"simulation":[94,114],"study":[95],"this":[97],"framework.":[98],"reconstruct":[100],"mathematical":[102],"model,":[103],"describe":[104],"its":[105],"fully":[106],"LGN-driven":[107],"update":[108],"rule,":[109],"implement":[111],"vectorized":[113],"framework":[115],"preserves":[117],"original":[119],"clique":[121],"operations":[122],"while":[123],"making":[124],"systematic":[125],"parameter":[126],"sweeps":[127],"feasible.":[128],"evaluate":[130],"using":[133,158],"tuning":[135],"curves,":[136],"an":[137],"index":[140],"(OSI),":[141],"controlled":[142],"noise":[144],"perturbations,":[145],"contrast":[146],"tests,":[147],"model-variant":[149],"comparisons.":[150],"further":[152],"add":[153],"spiking":[155,203],"SCI-layer":[156],"realization":[157,204],"leaky":[159],"integrate-and-fire":[160],"Hodgkin-Huxley":[162],"neurons":[163],"to":[164,194],"examine":[165],"whether":[166],"rate-coded":[168],"SCI":[169],"field":[170],"be":[172],"expressed":[173],"temporally":[175],"neural":[177],"activity.":[178],"simulations":[180],"support":[181],"qualitative":[184],"behavior":[185],"framework:":[189],"sharp":[190],"selectivity,":[192],"robustness":[193],"moderate":[195],"noise,":[197],"biologically":[200],"interpretable":[201],"proof-of-concept":[202],"inferred":[207],"field.":[209]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-08-15T00:00:00"}
