{"id":"W4402511305","doi":"10.1101/2024.09.09.611671","title":"Burst firing optimizes invariant coding of natural communication signals by electrosensory neural populations","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Coding (social sciences); Invariant (physics); Neural coding; Computer science; Artificial intelligence; Mathematics; 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":[0.000269774,0.0001693574,0.0002094217,0.0001764537,0.0001108095,0.0003682068,0.000295539,0.0001783364,0.0006743184],"category_scores_gemma":[0.001044373,0.0001223389,0.0001716843,0.0001312168,0.0003670541,0.0003530399,0.0004114082,0.0002647823,0.00007507619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003520175,"about_ca_system_score_gemma":0.0001422088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003554249,"about_ca_topic_score_gemma":0.0003705104,"domain_scores_codex":[0.99992,0.00001841734,0.000006571385,0.00002220649,0.00001562214,0.0000172066],"domain_scores_gemma":[0.9996759,0.0001339704,0.00007612475,0.00003558109,0.00003616493,0.00004235978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000351586,0.00009855636,0.005454567,0.00008565486,0.00006781294,0.0001598904,0.0001944852,0.1169498,0.8110615,0.0201946,0.0003915205,0.04499017],"study_design_scores_gemma":[0.00002070389,0.0001584679,0.009912537,0.000008191136,0.00002577884,0.0001205453,0.0000591623,0.9278063,0.04601185,0.01546058,0.0003980674,0.00001791403],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8281059,0.00008659665,0.1698586,0.00007821752,0.00001370814,0.00001388626,0.00005177569,0.0001301864,0.001660989],"genre_scores_gemma":[0.9941906,0.00002106172,0.005505486,0.00001091461,0.000004004597,0.000008033325,0.00001750658,0.00001939098,0.0002230267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006743184,"threshold_uncertainty_score":0.002553999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0220284571922487,"score_gpt":0.2442994494688866,"score_spread":0.2222709922766379,"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."}}