Temporal alignment and latent Gaussian process factor inference in population spike trains. - Lea Duncker; Maneesh Sahani (NeurIPS, 2018)

Learning interpretable continuous-time models of latent stochastic dynamical systems. - Lea Duncker; Gergo Bohner; Julien Boussard; Maneesh Sahani (ICML, 2019)

Organizing recurrent network dynamics by task-computation to enable continual learning. - Lea Duncker; Laura Driscoll; Krishna V. Shenoy; Maneesh Sahani; David Sussillo (NeurIPS, 2020)

Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs. - Julia Costacurta; Lea Duncker; Blue Sheffer; Winthrop Gillis; Caleb Weinreb; Jeffrey E. Markowitz; Sandeep R. Datta; Alex H. Williams; Scott W. Linderman (NeurIPS, 2022)

Scalable Variational Inference for Low-Rank Spatiotemporal Receptive Fields. - Lea Duncker; Kiersten M. Ruda; Greg D. Field; Jonathan W. Pillow (Neural Comput., 2023)

Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems. - Amber Hu; David M. Zoltowski; Aditya Nair; David Anderson; Lea Duncker; Scott W. Linderman (NeurIPS, 2024)