{"id":"W4416055450","doi":"10.1093/cercor/bhaf295","title":"Building on models—a perspective for computational neuroscience","year":2025,"lang":"en","type":"article","venue":"Cerebral Cortex","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Research Council; Engineering and Physical Sciences Research Council; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Forschungszentrum Jülich; Bundesministerium für Bildung und Forschung; Fundação de Amparo à Pesquisa do Estado de São Paulo; Deutsche Forschungsgemeinschaft; Natural Sciences and Engineering Research Council of Canada; RWTH Aachen University; European Commission; Government of Ontario","keywords":"Computational neuroscience; Computational model; Neuromorphic engineering; Perspective (graphical); Systems neuroscience; Block (permutation group theory); Correctness; Benchmark (surveying)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005947454,0.00157739,0.001832981,0.001611135,0.001390643,0.006562975,0.00431948,0.004467634,0.004823496],"category_scores_gemma":[0.01115092,0.0008681464,0.001858542,0.0009911215,0.010649,0.01710202,0.00368624,0.01090375,0.001452349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003475871,"about_ca_system_score_gemma":0.002963312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003737609,"about_ca_topic_score_gemma":0.00206728,"domain_scores_codex":[0.9971235,0.001807664,0.0001203522,0.0002776682,0.0005304586,0.0001401828],"domain_scores_gemma":[0.9914663,0.005858226,0.0002089856,0.001435081,0.0006794455,0.0003519478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004915878,0.00001107328,0.00004907646,0.00007712415,0.00001218345,0.0000143598,0.0001379591,0.00482426,0.00007736215,0.9865535,0.003155029,0.005083112],"study_design_scores_gemma":[0.000003606568,0.000006941375,0.00001581782,0.00006995207,0.000003719357,0.0000116419,0.00006838704,0.008009879,0.00005760645,0.9698908,0.0218542,0.000007397975],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00612483,0.03849993,0.7324477,0.1436124,0.002655668,0.000100043,0.0003052252,0.0009657177,0.07528847],"genre_scores_gemma":[0.3446933,0.06909567,0.5425953,0.01613786,0.005949607,0.0008758813,0.0009913042,0.001111205,0.01854992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006562975,"threshold_uncertainty_score":0.03145355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02463626378916327,"score_gpt":0.2947754391518078,"score_spread":0.2701391753626445,"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."}}