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Related Experiment Video

Updated: Oct 6, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Probabilistic Deterministic Finite Automata and Recurrent Networks, Revisited.

Sarah E Marzen1, James P Crutchfield2

  • 1W. M. Keck Science Department, Pitzer, Scripps, and Claremont McKenna College, Claremont, CA 91711, USA.

Entropy (Basel, Switzerland)
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

Reservoir computers (RCs) and Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) were tested for predicting probabilistic deterministic finite-state automata (PDFA) processes. LSTMs surprisingly excel at lossy feature extraction over predictive accuracy in low-data scenarios.

Keywords:
finite state machineshidden Markov modelslong short-term memoryrecurrent neural networksreservoir computerstime series prediction

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Area of Science:

  • Computational neuroscience
  • Machine learning theory
  • Information theory

Background:

  • Recurrent neural networks (RNNs) and reservoir computers (RCs) theoretically mimic finite-state automata.
  • Understanding their practical predictive capabilities, especially in data-limited scenarios, remains an active research area.

Purpose of the Study:

  • To evaluate the predictive performance of generalized linear models, RCs, and Long Short-Term Memory (LSTM) RNNs.
  • To assess these models' ability to predict stochastic processes from probabilistic deterministic finite-state automata (PDFA) in the small-data limit.
  • To analyze predictive accuracy and information-theoretic feature extraction using a rate-distortion curve.

Main Methods:

  • Systematic enumeration of PDFAs to create benchmark datasets.
  • Generation of stochastic processes from PDFAs with known randomness and correlation structures.
  • Computation of optimal memory-limited predictors for PDFAs.
  • Testing generalized linear models, RCs, and LSTMs on PDFA-generated data.
  • Evaluation using predictive accuracy and distance to the predictive rate-distortion curve.

Main Results:

  • Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) showed a surprising decrease in predictive accuracy with limited data.
  • LSTMs demonstrated superior performance in lossy predictive feature extraction, as measured by their proximity to the rate-distortion curve.
  • Generalized linear models and reservoir computers were also evaluated against these metrics.

Conclusions:

  • The study highlights the trade-offs between predictive accuracy and information-theoretic feature extraction in RNNs under data scarcity.
  • Causal states are identified as a valuable concept for understanding the predictive capabilities of RNNs.
  • The findings underscore the importance of considering model architecture and data availability when assessing predictive performance.