K. Cora Ames

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 signals for both arms are shared across hemispheres but partitioned in population-level activity to avoid conflicting movements.

The paper shows that neural dynamics can be accurately estimated without spike sorting by using multi-unit threshold crossings, enabling new analyses and electrode array design.