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

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 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, 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 nonparametric Bayesian HDP-HMM method can efficiently perform model selection and identify model parameters, which can used for modeling latent-state neuronal population dynamics.

Deciphering Neural Codes of Memory during Sleep

“…In other non-spatial tasks, the latent states may also accommodate non-spatial features of experiences or distinct behavioral patterns that cannot be measured directly. The connection between latent states and spatiotemporal spiking patterns can be established from statistical inference, hypothesis testing, and Monte Carlo shuffled statistics [66–68]. Furthermore, additional features (such as spiking synchrony or LFP features in terms of power or instantaneous phase) can be incorporated into the statistical model for further disassociating distinct latent states.…”

“…The population-decoding approach makes certain statistical assumptions about the population spike activity (e.g., independent Poisson assumption) and employs likelihood or Bayesian inference to decode the content of population codes. One class of decoding approach is supervised, which requires the receptive field information about individual neurons [64,65]; another class of decoding approach is unsupervised, which requires no receptive field or behavior measure [66–68] ( Fig. 4b ).…”

Spatiotemporal patterns of rodent hippocampal field potentials uncover spatial representations

“…Position-averaging on the feature matrix derived from NMF or RICA revealed localized structures in the latent state space. From the lower-rank MUA features (derived from NMF or RICA, followed by feature resampling; see Methods and Figure S5), we further trained an unsupervised Bayesian hidden Markov model (HMM) (Linderman et al, 2016) and inferred the latent state trajectories (Figure 3C). During run, the lower-rank MUA-inferred state trajectories matched well with the animal's position as well as spike-inferred state trajectories ( Figure 3D).…”

A hidden Markov model for decoding and the analysis of replay in spike trains

“…Thus, Chen et al (2012) are able to elicit directly from a spike train ensemble the distinct patterns of activity in place cells that may encode position, without needing to prespecify the receptive fields of these cells (the place fields , as would be necessary in a nonparametric approach), and to infer from the transition matrix the “topology” of the spatial representation. More recently, Linderman et al (2016), have used Dirichlet process techniques to handle the unknown number of states in a hidden Markov model.…”