{"id":"W2149661384","doi":"10.1109/iembs.1995.579785","title":"Reconstructing dynamics from neural spike trains","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Attractor; Spike (software development); Computer science; Dynamics (music); Point process; Spike train; Embedding; Biological neuron model; Neuron; Series (stratigraphy); Biological system; Sampling (signal processing); Process (computing); Statistical physics; Artificial intelligence; Algorithm; Control theory (sociology); Artificial neural network; Mathematics; Neuroscience; Physics; Mathematical analysis; Computer vision; Acoustics; Biology; Statistics","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":[0.0004256403,0.0005496466,0.0004381993,0.0007920197,0.000244484,0.0006061852,0.0004409411,0.0006091711,0.0008434159],"category_scores_gemma":[0.003034103,0.0005191482,0.0004760181,0.0004505339,0.0005224155,0.0009723878,0.0005216452,0.000854123,0.0002321842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00043469,"about_ca_system_score_gemma":0.0004085791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003113532,"about_ca_topic_score_gemma":0.001999364,"domain_scores_codex":[0.9998976,0.00002241818,0.000008090248,0.00001908443,0.00003650425,0.00001625302],"domain_scores_gemma":[0.9993684,0.0003034069,0.00009123429,0.0001088059,0.00008361046,0.00004451304],"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":[0.00017305,0.00002770445,0.002265937,0.0000938967,0.00005283251,0.0002780932,0.0001839252,0.887552,0.02794338,0.02671919,0.000696835,0.05401314],"study_design_scores_gemma":[0.000003665386,0.000007323158,0.0002540307,0.000003078257,0.000002451688,0.0000264054,0.000008594549,0.9913684,0.002003242,0.006111493,0.00020628,0.000005091291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.291994,0.0002387767,0.7051848,0.0002404381,0.00005234004,0.00002938492,0.0002448511,0.0007168401,0.001298609],"genre_scores_gemma":[0.8869933,0.0004482804,0.1101323,0.00003040601,0.00003889284,0.00003013976,0.0005846109,0.000117777,0.001624302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003113532,"threshold_uncertainty_score":0.006190836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05083429316386503,"score_gpt":0.2280122188681358,"score_spread":0.1771779257042707,"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."}}