{"id":"W2963345437","doi":"10.1523/eneuro.0108-19.2019","title":"Cellular and Network Mechanisms May Generate Sparse Coding of Sequential Object Encounters in Hippocampal-Like Circuits","year":2019,"lang":"en","type":"article","venue":"eNeuro","topic":"Fish biology, ecology, and behavior","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Neuroscience; Electric fish; Hippocampal formation; ENCODE; Neural coding; Decoding methods; Biology; Computer science; Fish <Actinopterygii>; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003122222,0.000128796,0.0002027601,0.00003309217,0.00004197148,0.00001094048,0.0001568082,0.0001062926,0.0009482873],"category_scores_gemma":[0.000009755508,0.0001245121,0.00003635293,0.0001422919,0.0001247637,0.00007693901,0.0001425131,0.0001291662,0.0001242764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004987167,"about_ca_system_score_gemma":0.000009294993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001060816,"about_ca_topic_score_gemma":0.0002688525,"domain_scores_codex":[0.9988873,0.0001227943,0.0002079385,0.0003518273,0.0001091924,0.0003209268],"domain_scores_gemma":[0.9996322,0.00004402221,0.00008426263,0.0001805365,0.000003362033,0.00005563475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000008889688,0.00003025885,0.2770436,0.00000683136,0.000003143265,0.00003070369,0.00007234421,0.001040797,0.721069,0.00005783627,0.0002615157,0.0003750607],"study_design_scores_gemma":[0.00101463,0.0004698818,0.8947691,0.00002739312,0.00003502907,0.00003357617,0.0001051993,0.001505604,0.09943858,0.0006379317,0.001554678,0.0004083673],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975304,0.00003183587,0.0001107164,0.00003887517,0.000997941,0.0002117275,0.00000583599,0.00001560621,0.001057078],"genre_scores_gemma":[0.99894,0.00004219284,0.0002046296,0.0004034557,0.00003998263,0.000008350107,0.000006016822,0.00001303092,0.0003423647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6216304,"threshold_uncertainty_score":0.999965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649638519820068,"score_gpt":0.221213198291701,"score_spread":0.2047168130935003,"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."}}