Lea Duncker

The paper develops a corticostriatal neural network model to show how cortical input compression enables distinct control and dynamic functions in the striatum across time-sensitive tasks.

The paper introduces a scalable variational inference method for estimating low-rank spatio-temporal receptive fields in neural data, showing improved performance over existing estimators.

Optogenetic and electrical perturbations in primate motor cortex during reaching show that movement-related neural activity is governed by a self-contained, low-dimensional dynamical subspace causally linked to reach kinematics.

This paper introduces a scalable method for analyzing neural population spike trains by extending Gaussian Process Factor Analysis to handle continuous time data and capture trial-to-trial variability.