{"id":"W2931400272","doi":"10.18122/td.1782.boisestate","title":"Deep Convolutional Spiking Neural Networks for Image Classification","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Education, India; International Institute of Information Technology, Hyderabad; Canadian Institute for Advanced Research","keywords":"MNIST database; Artificial intelligence; Spiking neural network; Computer science; Stochastic gradient descent; Artificial neural network; Backpropagation; Forgetting; Pattern recognition (psychology); Convolutional neural network; Feature (linguistics); Gradient descent; Deep learning; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008732618,0.0002232952,0.0002271051,0.0000426407,0.00009673293,0.00008906628,0.0001552666,0.0001904653,0.00004355967],"category_scores_gemma":[0.00003413275,0.0002460766,0.0001535057,0.00006468254,0.00002360751,0.0001164562,0.000154369,0.0005228139,0.000001839123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008807721,"about_ca_system_score_gemma":0.00001422595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.274249e-7,"about_ca_topic_score_gemma":0.000005570161,"domain_scores_codex":[0.9990068,0.00001763338,0.0002805782,0.0003301122,0.0000893341,0.0002755353],"domain_scores_gemma":[0.9994115,0.0001352669,0.0000604893,0.0002400312,0.00009521832,0.0000574583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004696291,0.000005298023,0.00001766439,0.0001553561,0.00002169892,0.000004374574,0.00002277616,0.9864033,0.004659357,0.0004757999,0.0001299125,0.008099799],"study_design_scores_gemma":[0.0001315662,0.000005971233,0.0004406817,0.00004959389,0.00001944916,0.00001009546,0.00005319951,0.9966147,0.001846837,0.0003815502,0.0001835732,0.0002627866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03692294,0.0007451821,0.958541,0.00005283062,0.001913376,0.0003061259,0.000003933125,0.0005034519,0.001011206],"genre_scores_gemma":[0.9660758,0.0000435491,0.03247192,0.00008199432,0.0008026478,0.00007098336,0.000331576,0.00004852205,0.00007297815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9291529,"threshold_uncertainty_score":0.9999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03491398023910249,"score_gpt":0.2699561131042656,"score_spread":0.2350421328651631,"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."}}