4 papers
Learning infinite context windows in recurrent architectures via spatial neural computing
Aleix Salvador-Pomarol, Arthur N. Montanari, Earl K. Miller +2
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 va…
Mean-field theory of rich oscillatory dynamics in low-rank recurrent networks with activity-dependent adaptation
Bowen W. Zheng, Earl K. Miller, Ila R. Fiete
We develop a dynamical mean-field theory for random recurrent networks with low-rank structure and firing-rate-driven adaptation. When the random connectivity is strong enough to g…
Scientific Machine Learning of Chaotic Systems Learns Reduced-Order Equations for Neural Populations
Anthony G. Chesebro, David Hofmann, Vaibhav Dixit +6
Extracting interpretable mathematical models from complex dynamical systems is difficult, especially for chaotic dynamics observed with noisy experimental data. We present PEM-UDE,…
Characterizing control between interacting subsystems with deep Jacobian estimation
Adam J. Eisen, Mitchell Ostrow, Sarthak Chandra +3
Biological function arises through the dynamical interactions of multiple subsystems, including those between brain areas, within gene regulatory networks, and more. A common appro…