{"id":"W4413972371","doi":"10.3389/fnins.2025.1634652","title":"Mapping the computational similarity of individual neurons within large-scale ensemble recordings using the SIMNETS analysis framework","year":2025,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; National Institutes of Health; Killam Trusts; U.S. Department of Veterans Affairs","keywords":"Similarity (geometry); Scale (ratio); Computer science; Artificial intelligence; Pattern recognition (psychology); Cartography; Geography; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001107912,0.0008234352,0.0005702793,0.001779084,0.000455113,0.001262321,0.001099728,0.0005702589,0.001775901],"category_scores_gemma":[0.004691694,0.0003185148,0.001050797,0.001196004,0.0007749858,0.001511226,0.001491217,0.0009379578,0.0003880755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007579839,"about_ca_system_score_gemma":0.0009611904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00339503,"about_ca_topic_score_gemma":0.00508017,"domain_scores_codex":[0.9995527,0.00009886906,0.00003147677,0.0001386672,0.0001353188,0.00004298702],"domain_scores_gemma":[0.9988392,0.0005104843,0.0002464133,0.000176395,0.0001619647,0.00006566496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000444092,0.0001858912,0.022115,0.0004607546,0.0006182473,0.0005910546,0.0005830369,0.6301656,0.08521392,0.04920316,0.004135565,0.2062836],"study_design_scores_gemma":[0.000008851184,0.00005158222,0.005200769,0.00001468189,0.00002748532,0.0001116414,0.00008351272,0.9611011,0.007905371,0.0243367,0.001137969,0.00002041063],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09489933,0.000179518,0.9005255,0.0001653143,0.00003070898,0.00007619153,0.0008197401,0.001820375,0.001483342],"genre_scores_gemma":[0.7063792,0.0003018454,0.2893253,0.00009459033,0.00005665304,0.0002886657,0.002006466,0.0004443675,0.001102951],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00339503,"threshold_uncertainty_score":0.006750524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02999965261100258,"score_gpt":0.2787688058592512,"score_spread":0.2487691532482486,"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."}}