Online Program Home
  My Program

Abstract Details

Activity Number: 549 - Recent Development of Statistical Learning Methods for Complex Biomedical Data
Type: Invited
Date/Time: Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #322053
Title: Statistical Modeling of Large-Scale Neural Ensembles
Author(s): Shizhe Chen* and Ali Shojaie and Eric Shea-Brown and Daniela Witten
Companies: Columbia University and University of Washington and University of Washington and University of Washington
Keywords: Hawkes process ; Weak dependence ; High-dimensionality
Abstract:

Thanks to advances in imaging technology and genetic engineering, it is now possible to record neural spiking activity at cellular resolution on live subjects. Such data offer opportunities for deeper understanding of how the brain works, but, on the other hand, they are often challenging to analyse: A typical recording consists of spike times of hundreds or thousands of neurons during a very limited time period, while the observed neuronal activity is temporally dependent with an unknown dependence structure. In this talk, we consider the task of modelling large-scale neural ensembles using the multivariate Hawkes process. The Hawkes process is a point process where a past event might affect the occurrence of future events. We propose an efficient procedure for estimating the sparse graphical structure encoded in the Hawkes process. We demonstrate the use of this procedure on both synthetic data and real data. Finally, we extend the existing theory on Hawkes process to allow for non-excitatory relationships, and show that the proposed procedure recovers the true graph with high probability.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association