Abstract:We report an improvement to the conventional Echo State Network (ESN), which already achieves competitive performance in one-dimensional time series prediction of dynamical systems. Our model -a 20%-dense ESN with reservoir weights derived from a fruit fly connectome (and from its bootstrapped distribution) -yields superior performance on a chaotic time series prediction task, and furthermore alleviates the ESN's high-variance problem. We also find that an arbitrary positioning of weights can degrade ESN perfo… Show more
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