{"id":"W4309263363","doi":"10.1109/biocas54905.2022.9948580","title":"Spike Compression through Selective Downsampling and Piecewise Curve Fitting Dedicated to Neural Recording Brain Implants","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Biomedical Circuits and Systems Conference (BioCAS)","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Upsampling; Spike (software development); Brain implant; Computer science; CMOS; Piecewise; Artificial neural network; Data compression; Companding; Neuromorphic engineering; Application-specific integrated circuit; Artificial intelligence; Algorithm; Electronic engineering; Channel (broadcasting); Computer hardware; Mathematics; Engineering; Telecommunications","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":[0.000291589,0.0005693514,0.0003398672,0.0006103041,0.0001995112,0.0004099272,0.0006123103,0.0003486455,0.001740332],"category_scores_gemma":[0.00132219,0.00024391,0.0004020229,0.0008699806,0.0003370275,0.0006065701,0.0004216593,0.0005642336,0.0004959941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002892786,"about_ca_system_score_gemma":0.0003497509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008118017,"about_ca_topic_score_gemma":0.001021024,"domain_scores_codex":[0.9996953,0.00003904136,0.00001784723,0.00005083913,0.0001745872,0.00002243051],"domain_scores_gemma":[0.9995116,0.0002062857,0.00005305672,0.0001084142,0.0001045379,0.00001612546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003739399,0.00006477528,0.0008240328,0.0002179852,0.00006688439,0.0001631172,0.0001960863,0.02611699,0.3919592,0.006377892,0.001391987,0.5722471],"study_design_scores_gemma":[0.00001943067,0.0002491582,0.002631611,0.00002135976,0.00006002674,0.0007871416,0.00005716953,0.5047755,0.4762728,0.003757933,0.01132092,0.0000469915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02097535,0.0002255405,0.977178,0.0000675903,0.00002936178,0.00002900136,0.00005553785,0.0007319907,0.0007076115],"genre_scores_gemma":[0.2894313,0.0006682192,0.7061828,0.00007868044,0.00009012028,0.00009092331,0.0003264923,0.0002923783,0.002839173],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001740332,"threshold_uncertainty_score":0.005822003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0861887668677823,"score_gpt":0.2963537313995677,"score_spread":0.2101649645317854,"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."}}