4 papers
Information-theoretic receding-horizon active learning of nonlinear dynamical systems
Juncal Arbelaiz, Anushri Arora, Jonathan W. Pillow
Accurately learning nonlinear dynamics from a finite-duration experiment requires the efficient collection of informative data. We address this challenge for stochastic controlled…
Flexible inference for animal learning rules using neural networks
Yuhan Helena Liu, Victor Geadah, Jonathan Pillow
Understanding how animals learn is a central challenge in neuroscience, with growing relevance to the development of animal- or human-aligned artificial intelligence. However, exis…
Inferring System and Optimal Control Parameters of Closed-Loop Systems from Partial Observations
Victor Geadah, Juncal Arbelaiz, Harrison Ritz +3
We consider the joint problem of system identification and inverse optimal control for discrete-time stochastic Linear Quadratic Regulators. We analyze finite and infinite time hor…
Modeling Neural Activity with Conditionally Linear Dynamical Systems
Victor Geadah, Amin Nejatbakhsh, David Lipshutz +2
Neural population activity exhibits complex, nonlinear dynamics, varying in time, over trials, and across experimental conditions. Here, we develop Conditionally Linear Dynamical S…