A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation
Name
CBMM-Memo-027.pdf
Size
9.44 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Linderman, Scott W.
Johnson, Matthew J.
Wilson, Matthew A.
Chen, Zhe
Date Issued
December 1, 2014
Publisher
Center for Brains, Minds and Machines (CBMM), arXiv
Citation
arXiv:1411.7706v1
Series/Report no.
CBMM Memo Series;027
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.
Description
This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216.
Subjects
Hippocampus
Neuroscience
Spatial Navigation
Rodent
Bayesian
Terms of Use
Attribution-NonCommercial 3.0 United States
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