{"id":"W4407278427","doi":"10.1101/2025.02.07.637062","title":"Computation-through-Dynamics Toolkit: Simulated datasets and quality metrics for dynamical models of neural activity","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"National Institute of Neurological Disorders and Stroke; National Institute on Drug Abuse; National Science Foundation; National Institutes of Health; Wu Tsai Neurosciences Institute, Stanford University; Fonds de recherche du Québec – Nature et technologies; Emory University; Meta","keywords":"Computer science; Benchmark (surveying); Computation; Artificial neural network; Artificial intelligence; Machine learning; Field (mathematics); Models of neural computation; System dynamics; Algorithm; Mathematics","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":[0.006329758,0.001501213,0.0009613315,0.002432303,0.0008852278,0.002150276,0.004386404,0.00248494,0.006535372],"category_scores_gemma":[0.03903402,0.0007470876,0.001982496,0.0021061,0.001413547,0.002061646,0.002158041,0.002996562,0.001934008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002087001,"about_ca_system_score_gemma":0.002924922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01900963,"about_ca_topic_score_gemma":0.02500496,"domain_scores_codex":[0.9978047,0.001000385,0.0002263368,0.0003383938,0.0005055164,0.0001246289],"domain_scores_gemma":[0.980306,0.01356337,0.0008740117,0.002939839,0.001758556,0.0005582134],"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":[0.0003877263,0.0002502726,0.008901663,0.0008302616,0.0003155534,0.0001928547,0.0001973548,0.8873854,0.001071533,0.01757838,0.06386407,0.01902497],"study_design_scores_gemma":[0.00009372246,0.00004703089,0.001115261,0.0000540316,0.00001757944,0.00003882538,0.00003381576,0.978109,0.001136022,0.01301933,0.006296731,0.00003863638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3040612,0.002545096,0.4070618,0.004828964,0.000948047,0.0009522388,0.1538371,0.1134763,0.01228941],"genre_scores_gemma":[0.5915012,0.0005982416,0.2506174,0.0008627562,0.0001316201,0.002241589,0.1417783,0.009826701,0.002442114],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01900963,"threshold_uncertainty_score":0.03779793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06054169163066898,"score_gpt":0.3046456054384745,"score_spread":0.2441039138078056,"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."}}