Point process models for sequence detection in neural spike trains

Seminar Series

Friday, October 23, 2020 - 03:00
Zoom
Scott Linderman, PhD

Abstract:   Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning. Discovering these sequences in an unsupervised manner is a longstanding problem in statistical neuroscience. I will present our new work using Neyman-Scott processes—a class of doubly stochastic point processes—to model sequences as a set of latent, continuous-time, marked events that produce cascades of neural spikes. This sparse representation of sequences opens new possibilities for spike train modeling. For example, we introduce learnable time warping parameters to model sequences of varying duration, as have been experimentally observed in neural circuits.  Bayesian inference in this model requires integrating over the set of latent events, akin to inference in mixture of finite mixture (MFM) models. I will show how recent work on MFMs can be adapted to develop a collapsed Gibbs sampling algorithm for Neyman-Scott processes.  Finally, I will present an empirical assessment of the model and algorithm on spike-train recordings from songbird higher vocal center and rodent hippocampus.

Scott Linderman, PhD
Assistant Professor of Statistics
Stanford University