{"id":"W2757874534","doi":"10.1109/mwscas.2017.8052937","title":"Fully discrete-time neural recording front-ends: Feasibility and design considerations","year":2017,"lang":"en","type":"article","venue":"","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Scalability; Front and back ends; Front (military); Scaling; Artificial neural network; Power (physics); Channel (broadcasting); Electronic engineering; Engineering; Artificial intelligence; Telecommunications; Physics","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":[0.0006913586,0.000385942,0.0002990424,0.0001717573,0.0002825955,0.001376417,0.0009599476,0.001346709,0.003481895],"category_scores_gemma":[0.001900333,0.0002658435,0.0002662364,0.0001271524,0.000492298,0.001859288,0.0003607047,0.0007234538,0.001097113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004545241,"about_ca_system_score_gemma":0.0003687025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004069385,"about_ca_topic_score_gemma":0.0007284866,"domain_scores_codex":[0.9996309,0.00007004157,0.00001938926,0.00004851226,0.0001984211,0.00003270046],"domain_scores_gemma":[0.9987171,0.0007572819,0.00007741709,0.0001172275,0.0002967746,0.00003429837],"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":[0.0007075805,0.0002582032,0.001363696,0.001580153,0.00007872956,0.0006917351,0.0004714619,0.09657997,0.5150641,0.1989971,0.003422987,0.1807842],"study_design_scores_gemma":[0.0001174237,0.00157429,0.001523784,0.0002553985,0.00007481512,0.001367443,0.0002149142,0.6635476,0.2478298,0.04584796,0.03754867,0.00009795801],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04965114,0.00156507,0.9316785,0.001197191,0.0001393521,0.0001224094,0.0001118419,0.0005003944,0.0150341],"genre_scores_gemma":[0.6238193,0.001623563,0.3649068,0.0004017205,0.0001503177,0.0001850966,0.0001316988,0.0000767092,0.008704829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003481895,"threshold_uncertainty_score":0.01164812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.12799319593802,"score_gpt":0.3147115823520519,"score_spread":0.1867183864140319,"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."}}