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Complexity-calibrated Benchmarks for Machine Learning Reveal When Next-Generation Reservoir Computer Predictions Succeed and Mislead
One conclusion is that large probabilistic state machines -- specifically, large $\epsilon$-machines -- are key to generating challenging and structurally-unbiased stimuli for ground-truthing recurrent neural network architectures.
Nearly Maximally Predictive Features and Their Dimensions
Scientific explanation often requires inferring maximally predictive features from a given data set.
Time Resolution Dependence of Information Measures for Spiking Neurons: Atoms, Scaling, and Universality
A first step towards that larger goal is to develop information measures for individual output processes, including information generation (entropy rate), stored information (statistical complexity), predictable information (excess entropy), and active information accumulation (bound information rate).
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