Stephen Keeley

Keeley received Bachelor's degrees in physics and neuroscience from the University of Rochester, his Ph.D. from the Center for Neural Science at New York University, and did his postdoctoral work at Princeton University.

My lab develops new machine learning and statistical methods to help understand neural processing and behavior. We work with collaborators from institutes around the world to analyze real neural datasets recorded in a variety of experimental settings and animal models, including datasets from flies, rodents, and humans. Current research topics include latent variable models, dimensionality reduction, neural dynamical systems, multi-region neural communication and human decision-making.

A semi-parametric model for decision making in high-dimensional sensory discrimination tasks. Keeley, S., Letham, B., Sanders, C., Tymms, C., & Shvartsman, M. (2023).  Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 1, pp. 40-47). [abs]

Modeling statistical dependencies in multi-region spike train data. Keeley SL, Zoltowski DM, Aoi MC, & Pillow JW (2020) Current Opinion in Neurobiology 65: 194-202. [abs]

Identifying signal and noise structure in neural population activity with Gaussian process factor models. Keeley SL, Aoi MC, Yu Y, Smith SL, BR & Pillow JW (2020) Advances in Neural Information Processing Systems (NeurIPS) 33: 13795-13805. [abs]

Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations. Keeley SL, Zoltowski DM, Yu Y, Yates JL, Smith SL, & Pillow JW. (2020) Proceedings of the 37th International Conference on Machine Learning (ICML) 119:5177-5186. [abs]

Modeling fast and slow gamma oscillations with interneurons of different subtype. Keeley, S., Fenton, A. A., & Rinzel, J. (2017). Journal of neurophysiology, 117(3), 950-965. [paper]