38 citations · 101 across the 12 of their papers we have counts for
13 papers
Interpretable model-free inference of parametric variation across time-series data through large-scale feature extraction
Ben D. Fulcher, Carl H. Lubba, Giorgio F. Gilestro +2
Here we address the problem of estimating the dimensionality and nature of parametric variation in an unknown generative process directly from time-series data, without specifying…
Neural manifold analysis of brain circuit dynamics in health and disease
Rufus Mitchell-Heggs, Seigfred Prado, Giuseppe P. Gava +2
Recent developments in experimental neuroscience make it possible to simultaneously record the activity of thousands of neurons. However, the development of analysis approaches for…
catch22: CAnonical Time-series CHaracteristics
Carl H Lubba, Sarab S Sethi, Philip Knaute +3
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across…
Estimating Information-Theoretic Quantities
Robin A. A. Ince, Simon R. Schultz, Stefano Panzeri
Information theory is a practical and theoretical framework developed for the study of communication over noisy channels. Its probabilistic basis and capacity to relate statistical…
Applications of Information Theory to Analysis of Neural Data
Simon R. Schultz, Robin A. A. Ince, Stefano Panzeri
Information theory is a practical and theoretical framework developed for the study of communication over noisy channels. Its probabilistic basis and capacity to relate statistical…
Summary of Information Theoretic Quantities
Robin A. A. Ince, Stefano Panzeri, Simon R. Schultz
Information theory is a practical and theoretical framework developed for the study of communication over noisy channels. Its probabilistic basis and capacity to relate statistical…