{"id":"W2963630453","doi":"10.3389/fncom.2014.00123","title":"Structured chaos shapes spike-response noise entropy in balanced neural networks","year":2014,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Institute on Drug Abuse","keywords":"Chaotic; Computer science; Spike train; Statistical physics; Entropy (arrow of time); Spike (software development); Network dynamics; ENCODE; Artificial neural network; Artificial intelligence; Physics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003580802,0.0002108633,0.0003044266,0.0004450258,0.000244542,0.0006709456,0.0003015711,0.000339037,0.0007696677],"category_scores_gemma":[0.003143369,0.0002100781,0.0002127654,0.0001658619,0.0007650498,0.001013611,0.0005125803,0.0002733889,0.00009696873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005107735,"about_ca_system_score_gemma":0.0001872123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006922458,"about_ca_topic_score_gemma":0.0007829521,"domain_scores_codex":[0.9998887,0.00003301738,0.000006194583,0.0000194922,0.00002926132,0.00002343391],"domain_scores_gemma":[0.999096,0.0005135526,0.0002004158,0.00004474425,0.0000679572,0.00007722255],"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":[0.0003168567,0.0000581414,0.0118014,0.0001078232,0.00008029996,0.0004923296,0.0003514884,0.7463568,0.09650807,0.1323482,0.0005507478,0.0110278],"study_design_scores_gemma":[0.000009554917,0.0000278489,0.003310278,0.000007088832,0.00000619718,0.00004697949,0.00001873527,0.9518327,0.001711535,0.04291373,0.0001055706,0.0000097759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9112881,0.0001548088,0.0851546,0.0001958734,0.00001249591,0.00001298156,0.00007085851,0.0001075953,0.003002607],"genre_scores_gemma":[0.998468,0.00002960125,0.001276063,0.000008113457,0.000004116251,0.000006792508,0.00001617279,0.000009615857,0.0001813915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007696677,"threshold_uncertainty_score":0.003705919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1194656564285822,"score_gpt":0.4644464202671545,"score_spread":0.3449807638385723,"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."}}