- Bayesian entropy estimation for binary spike train data using parametric prior knowledge
(with Memming Park and Jonathan Pillow)
We formulate new Bayesian estimators for the entropy of binary spike trains, using priors designed to exploit the statistical structure of simultaneously-recorded spike responses. These estimators are computationally efficient, and show excellent performance on empirical data. This paper was selected for a Spotlight Presentation at the main conference!
- Universal models for binary spike patterns using centered Dirichlet processes
(with Memming Park, Kenneth Latimer, and Jonathan Pillow)
We propose a family of models (universal binary models, or UBM’s) capable of describing arbitrary distributions over all binary spike patterns. Combined with a good choice of parametric “base measure”, universal models are flexible, parsimonious, and computationally efficient. In application to data, we show that UBM’s to be a promising tool for studying the statistical structure of large-scale neural populations.
- Spectral methods for neural characterization using generalized quadratic models
(with Memming Park, Nicholas Priebe, and Jonathan Pillow)
We introduce a new class of single-neuron models we call the Generalized Quadratic Model, or GQM. While similar to the GLM, the GQM is closely related to methods for dimensionality-reduction often used in neuroscience (STA and STC). A model-based framework, and a few computational tricks based on a quantity known as the “expected log likelihood”, allow us to derive fast inference methods for both spike and analog data, and (in the analog case) for experiments with non-Gaussian stimuli.
For more information you can check out my publications page.