A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation
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Author(s)
Linderman, Scott W.
Johnson, Matthew J.
Chen, Zhe
Wilson, Matthew A
Date Issued
February 2016
Journal
Journal of Neuroscience Methods
Publisher
Elsevier
Citation
Linderman, Scott W. et al. “A Bayesian Nonparametric Approach for Uncovering Rat Hippocampal Population Codes During Spatial Navigation.” Journal of Neuroscience Methods 263 (April 2016): 36–47 © 2016 Elsevier B.V.
Version
Author's final manuscript
Abstract
Background: Rodent hippocampal population codes represent important spatial information about the environment during navigation. Computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. New method: We extend our previous work and propose a novel Bayesian nonparametric approach to infer rat hippocampal population codes during spatial navigation. To tackle the model selection problem, we leverage a Bayesian nonparametric model. Specifically, 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). Results: The effectiveness of our Bayesian approaches is demonstrated on recordings from a freely behaving rat navigating in an open field environment. Comparison with existing methods: The HDP-HMM outperforms the finite-state HMM in both simulated and experimental data. For HPD-HMM, the 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. Conclusion: The Bayesian nonparametric HDP-HMM method can efficiently perform model selection and identify model parameters, which can used for modeling latent-state neuronal population dynamics.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Picower Institute for Learning and Memory
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Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/J.JNEUMETH.2016.01.022
