{"id":"W4390993539","doi":"10.1109/biocas58349.2023.10388676","title":"Quantized Spiking Neural Networks on FPGA: An Application to Retinal Prosthetics","year":2023,"lang":"en","type":"article","venue":"","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Neuroprosthetics; Field-programmable gate array; Computer science; Artificial neural network; Spiking neural network; Retinal; Neural Prosthesis; Computer hardware; Artificial intelligence; Neuroscience; Psychology; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001739175,0.0001465986,0.0001139247,0.0001597446,0.000197503,0.0001031333,0.0003501884,0.0000377147,0.000007669366],"category_scores_gemma":[0.0001395669,0.0001260289,0.00003855898,0.001268585,0.0000382643,0.0001946077,0.00008660239,0.0001807654,0.0001887686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001717765,"about_ca_system_score_gemma":0.000006764742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004544834,"about_ca_topic_score_gemma":0.000002244506,"domain_scores_codex":[0.9984534,0.00004438393,0.0001765954,0.0005737211,0.0003111255,0.0004407729],"domain_scores_gemma":[0.9993264,0.0001064842,0.00003597991,0.0003528751,0.00001272594,0.0001655071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003709642,0.00002741211,0.00005975585,0.000003905393,1.551965e-7,0.00002625756,0.00004448153,0.08236835,0.8992069,0.00375024,0.0001387783,0.01433666],"study_design_scores_gemma":[0.0001093993,0.0002707494,0.0007480333,0.000006508451,0.000001433065,0.00001600171,0.00001166868,0.6889597,0.3083625,0.00005413351,0.001308947,0.0001509916],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870168,7.712055e-7,0.009018805,0.001124684,0.0004610255,0.0004532406,0.000001603369,0.0008728972,0.001050201],"genre_scores_gemma":[0.9965723,0.000007238869,0.00007396035,0.002806454,0.0001140379,0.0000514304,0.000001736693,0.00002761828,0.0003452931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6065913,"threshold_uncertainty_score":0.5139307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.048234788623459,"score_gpt":0.3073435626801798,"score_spread":0.2591087740567208,"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."}}