Abigail A. Russo

The paper shows that working memory tasks involve a shared low-dimensional neural structure with more disjoint firing fields compared to simpler tasks, supporting cognitive processes.

This paper introduces SCA, an unsupervised method for identifying interpretable latent factors in neural data, and shows its effectiveness across different datasets.

The paper shows that motor cortex activity during movement tasks reflects network-level strategies for generating muscle activity rather than direct muscle control.

The paper compares trajectory geometries in the supplementary motor area (SMA) and primary motor cortex (M1) to infer different computational roles, finding SMA trajectories are helical and M1 are elliptical.