Reservoir Computing
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Lyon, France, March 29-30. Cognitive scientists, roboticists and machine learning engineers who develop complex, neural-network-based architectures;. Computational neuroscientists who apply existing methods from dynamical systems theory to neural dynamics;.
Use a large, random RNN as an excitable medium - called a. In this context -, such that when driven by input signals, each unit in the RNN creates its own nonlinear transform of the input;. Output signals are read out from the excited RNN by some readout mechanism, typically a simple linear combination of the reservoir signals;. The main flavors of RC.
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Skip to Main Content. Reservoir Computing Shaping Dynamics into Information. And this overview article. Use a large, random RNN as an excitable medium - called a. In this context -, such that when driven by input signals, each unit in the RNN creates its own nonlinear transform of the input;. Output signals are read out from the excited RNN by some readout mechanism, typically a simple linear combination of the reservoir signals;. Temporal Recurrent Neural Network. The main flavors of RC. Hints for opti.CONTENT
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