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Foundations for a Circuit Complexity Theory of Sensory Processing., and . NIPS, page 259-265. MIT Press, (2000)On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing., , and . NIPS, page 1425-1432. Curran Associates, Inc., (2008)Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets, , , , , and . (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.Integration of nanoscale memristor synapses in neuromorphic computing architectures, , , , and . CoRR, (2013)Adversarially Robust Spiking Neural Networks Through Conversion., and . CoRR, (2023)Embodied Synaptic Plasticity with Online Reinforcement learning., , , , , , , , , and 1 other author(s). CoRR, (2020)Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons., , and . NIPS, page 713-720. MIT Press, (2006)Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning, , and . Cerebral Cortex, 24 (3): 677--690 (2014)Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype., , , , , , , , , and . CoRR, (2019)Embodied Synaptic Plasticity With Online Reinforcement Learning., , , , , , , , , and 1 other author(s). Frontiers Neurorobotics, (2019)