{"id":"W4413181688","doi":"10.3389/fncom.2025.1639829","title":"Maximum likelihood estimation of spatially dependent interactions in large populations of cortical neurons","year":2025,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Spike (software development); Evoked activity; Poisson distribution; Spatial ecology; Spike train; Scale (ratio); Cortical neurons; Artificial intelligence; Neuroscience; Mathematics; Biology; Cartography; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001734404,0.0005376138,0.0006392316,0.000599054,0.0003522628,0.0007767603,0.001036861,0.001078444,0.0007584087],"category_scores_gemma":[0.01028271,0.0005578054,0.0005075354,0.0005429887,0.0008809192,0.001253195,0.001096892,0.00103169,0.0002296352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004777074,"about_ca_system_score_gemma":0.0006030179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00155451,"about_ca_topic_score_gemma":0.001813725,"domain_scores_codex":[0.999545,0.0002287843,0.00002125452,0.00009169873,0.00007827812,0.00003500874],"domain_scores_gemma":[0.9961397,0.003203102,0.0002882079,0.0001521257,0.0001420334,0.00007485449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000732138,0.00003272292,0.002804203,0.00007258395,0.00007643061,0.0001634854,0.0001150603,0.954231,0.009157057,0.01496373,0.0004683637,0.01784229],"study_design_scores_gemma":[0.000004976549,0.000006237808,0.0005574976,0.000002850793,0.00000227225,0.00002270653,0.000007074411,0.9919017,0.0006315805,0.006774768,0.00008210489,0.000006094826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07691506,0.0001733195,0.9214811,0.000299538,0.000007382784,0.00002367536,0.00009636944,0.0002734393,0.0007301926],"genre_scores_gemma":[0.8834235,0.0001824521,0.1143275,0.0001026493,0.00003521977,0.0001425949,0.0003694777,0.0001174498,0.001299271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001734404,"threshold_uncertainty_score":0.009172499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02478461751246693,"score_gpt":0.3018134070359115,"score_spread":0.2770287895234446,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}