Homeostatic Plasticity for Single Node Delay-Coupled Reservoir Computing
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Publication:5380246
DOI10.1162/NECO_a_00737zbMath1473.68173OpenAlexW2134524435WikidataQ50591832 ScholiaQ50591832MaRDI QIDQ5380246
Gordon Pipa, Hazem Toutounji, Johannes M. Schumacher
Publication date: 4 June 2019
Published in: Neural Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1162/neco_a_00737
Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Time series analysis of dynamical systems (37M10) Networks and circuits as models of computation; circuit complexity (68Q06)
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Cites Work
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- Reservoir computing approaches to recurrent neural network training
- Bifurcation theory of functional differential equations
- The cerebellum as a liquid state machine
- Fading memory and time series prediction in recurrent networks with different forms of plasticity
- Optimization and applications of echo state networks with leaky- integrator neurons
- Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning
- Homeostasis and evolution together dealing with novelties and managing disruptions
- Fading memory and the problem of approximating nonlinear operators with Volterra series
- Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
- Homeostatic Plasticity for Single Node Delay-Coupled Reservoir Computing
- Polychronization: Computation with Spikes
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
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