Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time. - Feng Zhu; Andrew R. Sedler; Harrison A. Grier; Nauman Ahad; Mark A. Davenport; Matthew T. Kaufman; Andrea Giovannucci; Chethan Pandarinath (NeurIPS, 2021)

Expressive architectures enhance interpretability of dynamics-based neural population models. - Andrew R. Sedler; Christopher Versteeg; Chethan Pandarinath (CoRR, 2022)

Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity. - Christopher Versteeg; Andrew R. Sedler; Jonathan D. McCart; Chethan Pandarinath (NeurReps, 2023)

lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems. - Andrew R. Sedler; Chethan Pandarinath (CoRR, 2023)

General and interpretable models for inferring dynamical computation in biological neural networks. - Andrew R. Sedler (2023)

High-performance neural population dynamics modeling enabled by scalable computational infrastructure. - Aashish N. Patel; Andrew R. Sedler; Jingya Huang; Chethan Pandarinath; Vikash Gilja (J. Open Source Softw., 2023)

Diffusion-Based Generation of Neural Activity from Disentangled Latent Codes. - Jonathan D. McCart; Andrew R. Sedler; Christopher Versteeg; Domenick Mifsud; Mattia Rigotti-Thompson; Chethan Pandarinath (CoRR, 2024)