97 citations · 406 across the 28 of their papers we have counts for
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Prediction and Power in Molecular Sensors: Uncertainty and Dissipation When Conditionally Markovian Channels Are Driven by Semi-Markov Environments
Sarah E. Marzen, James P. Crutchfield
Sensors often serve at least two purposes: predicting their input and minimizing dissipated heat. However, determining whether or not a particular sensor is evolved or designed to…
The difference between memory and prediction in linear recurrent networks
Sarah Marzen
Recurrent networks are trained to memorize their input better, often in the hopes that such training will increase the ability of the network to predict. We show that networks desi…
Intrinsic computation of a Monod-Wyman-Changeux molecule
Sarah Marzen
Causal states are minimal sufficient statistics of prediction of a stochastic process, their coding cost is called statistical complexity, and the implied causal structure yields a…
Structure and Randomness of Continuous-Time Discrete-Event Processes
S. E. Marzen, J. P. Crutchfield
Loosely speaking, the Shannon entropy rate is used to gauge a stochastic process' intrinsic randomness; the statistical complexity gives the cost of predicting the process. We calc…
Nearly Maximally Predictive Features and Their Dimensions
Sarah E. Marzen, James P. Crutchfield
Scientific explanation often requires inferring maximally predictive features from a given data set. Unfortunately, the collection of minimal maximally predictive features for most…