{"id":"W4413762876","doi":"10.1101/2025.08.26.669250","title":"Topological spatial coding for rapid generalization in the hippocampal formation","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology; National Research Foundation of Korea; Institute for Information and Communications Technology Promotion; Electronics and Telecommunications Research Institute; National Research Foundation","keywords":"Generalization; Topology (electrical circuits); Coding (social sciences); Hippocampal formation; Computer science; Theoretical computer science; Mathematics; Neuroscience; Biology; Combinatorics","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":[0.0002151543,0.0001661573,0.0001903326,0.0003820327,0.0002769568,0.0006028311,0.0004626298,0.0003068109,0.001336168],"category_scores_gemma":[0.001820829,0.0001551292,0.0003217658,0.0003538051,0.001045624,0.001187432,0.0008897387,0.0006088162,0.0002243338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005063491,"about_ca_system_score_gemma":0.0004551517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001503031,"about_ca_topic_score_gemma":0.001852044,"domain_scores_codex":[0.9998924,0.00001999696,0.000007035378,0.00002819871,0.00003233833,0.00002013594],"domain_scores_gemma":[0.9995073,0.0001601492,0.00009485614,0.000134784,0.00006297515,0.00003982162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002080289,0.00005550369,0.003826453,0.0001277024,0.00004639493,0.0002181814,0.0002907998,0.2663933,0.05708529,0.4743736,0.00394639,0.1934283],"study_design_scores_gemma":[0.00001535577,0.00005102385,0.001748519,0.00001152952,0.00001175889,0.0001250978,0.00004789295,0.6740021,0.01327364,0.3088368,0.001856016,0.00002025238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2128992,0.0002731326,0.7792057,0.0004468569,0.00006091263,0.00001975129,0.0001830673,0.0006049584,0.006306397],"genre_scores_gemma":[0.9531395,0.0001557508,0.04517762,0.00005934273,0.00001911562,0.00002041253,0.0001243777,0.00006237513,0.001241445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001503031,"threshold_uncertainty_score":0.004469872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02774245832777227,"score_gpt":0.2564974875095342,"score_spread":0.2287550291817619,"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."}}