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Mesoscopic modeling of hidden spiking neurons., , , и . NeurIPS, (2022)Fitting summary statistics of neural data with a differentiable spiking network simulator., , , , и . NeurIPS, стр. 18552-18563. (2021)Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets, , , , , и . (2019)cite arxiv:1901.09049Comment: We changed in this version 2 of the paper the name of the new learning algorithms to e-prop, corrected minor errors, added details -- especially for resulting new rules for synaptic plasticity, edited the notation, and included new results for TIMIT.Creating Audio Based Experiments as Social Web Games with the CASimIR Framework., , , , и . Semantic Audio, Audio Engineering Society, (2014)Deep Rewiring: Training very sparse deep networks, , , и . (2017)cite arxiv:1711.05136Comment: Accepted for publication at ICLR 2018. 10 pages (12 with references, 24 with appendix), 4 Figures in the main text.Long short-term memory and Learning-to-learn in networks of spiking neurons., , , , и . NeurIPS, стр. 795-805. (2018)Local plasticity rules can learn deep representations using self-supervised contrastive predictions., , , и . NeurIPS, стр. 30365-30379. (2021)Deep Rewiring: Training very sparse deep networks., , , и . ICLR (Poster), OpenReview.net, (2018)