30 citations · 31 across the 2 of their papers we have counts for
5 papers
Learning Options for Compositional Motor Control with Adapter Banks
Sreejan Kumar, Marcelo Mattar, Lea Duncker
Learning flexible motor primitives is a hallmark of skilled motor control. Recent neuroscience theory proposes that motor primitives may be implemented as low-rank perturbations of…
A cross-species neural foundation model for end-to-end speech decoding
Yizi Zhang, Linyang He, Chaofei Fan +9
Speech brain-computer interfaces (BCIs) aim to restore communication for people with paralysis by translating neural activity into text. Most systems use cascaded frameworks that d…
Separating the what and how of compositional computation to enable reuse and continual learning
Haozhe Shan, Sun Minni, Lea Duncker
The ability to continually learn, retain and deploy skills to accomplish goals is a key feature of intelligent and efficient behavior. However, the neural mechanisms facilitating t…
Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
Amber Hu, David Zoltowski, Aditya Nair +3
Understanding how the collective activity of neural populations relates to computation and ultimately behavior is a key goal in neuroscience. To this end, statistical methods which…
Learning interpretable continuous-time models of latent stochastic dynamical systems
Lea Duncker, Gergo Bohner, Julien Boussard +1
We develop an approach to learn an interpretable semi-parametric model of a latent continuous-time stochastic dynamical system, assuming noisy high-dimensional outputs sampled at u…