A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation

TitleA Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation
Publication TypeJournal Article
Year of Publication2016
AuthorsLinderman, SW, Johnson, MJ, Wilson, MA, Chen, Z
JournalJournal of Neuroscience Methods
Volume263
Issue1
Start Page36
Type of ArticleComputational Neuroscience
Abstract

Rodent hippocampal population codes represent important spatial information about the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and propose a nonparametric Bayesian approach to infer rat hippocampal population codes during spatial navigation. To tackle the model selection problem, we leverage a nonparametric Bayesian model. Specifically, to analyze rat hippocampal ensemble spiking activity, we apply a hierarchical Dirichlet process-hidden Markov model (HDP-HMM) using two Bayesian inference methods, one based on Markov chain Monte Carlo (MCMC) and the other based on variational Bayes (VB). We demonstrate the effectiveness of our Bayesian approaches on recordings from a freely-behaving rat navigating in an open field environment. We find that MCMC-based inference with Hamiltonian Monte Carlo (HMC) hyperparameter sampling is flexible and efficient, and outperforms VB and MCMC approaches with hyperparameters set by empirical Bayes.

Download:  PDF icon Journal of Neuroscience Methods

Research Area: 

CBMM Relationship: 

  • CBMM Related