Author

E. K. Miller

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#machine learning Preprint Oct 2026

Learning infinite context windows in recurrent architectures via spatial neural computing

Recurrent neural networks (RNNs) offer linear-time scaling with sequence length while requiring only constant memory, yet they struggle to capture long-range dependencies due to vanishing gradients and limited receptive fields. To address these limitations, we introduce a second-order recurrent model in which the stand...

Aleix Salvador-Pomarol, A. Montanari, E. K. Miller et al. · 0 citations

#machine learning Preprint Jul 2025

Scientific Machine Learning of Chaotic Systems Learns Reduced-Order Equations for Neural Populations

PEM-UDE, a method that combines prediction-error methodology with universal differential equations to discover governing equations from limited, noise-corrupted observations, yields a multi-scale neural mass model that ties single-neuron parameters to macroscopic network dynamics and predicts a relationship between con...

Anthony G. Chesebro, David Hofmann, V. Dixit et al. · 1 citation