Search

Andrew R. Sedler

Stabilizing brain-computer interfaces through alignment of latent dynamics
Diffusion-based generation of neural activity from disentangled latent codes
Expressive architectures enhance interpretability of dynamics-based neural population models
Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity
High-performance neural population dynamics modeling enabled by scalable computational infrastructure
lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems
A large-scale neural network training framework for generalized estimation of single-trial population dynamics

Published with Hugo Blox Builder — the free, open source website builder that empowers creators.