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

• Bayesian nonparametric HDP-HMM can efficiently perform model selection and identify model complexity.
• MCMC inference outperforms other inference methods such as variational Bayes.
• The HDP-HMM and MCMC inference can efficiently uncover rat hippocampal population codes during spatial navigation.

BackgroundRodent 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 methodWe 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).ResultsThe effectiveness of our Bayesian approaches is demonstrated on recordings from a freely behaving rat navigating in an open field environment.Comparison with existing methodsThe 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.ConclusionThe Bayesian nonparametric HDP-HMM method can efficiently perform model selection and identify model parameters, which can used for modeling latent-state neuronal population dynamics.