Scott W. Linderman

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

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.

The Striatum Organizes 3D Behavior via Moment-to-Moment Action Selection

Many naturalistic behaviors are built from modular components that are expressed sequentially. Although striatal circuits have been implicated in action selection and implementation, the neural mechanisms that compose behavior in unrestrained animals are not well understood. Here, we record bulk and cellular neural activity in the direct and indirect pathways of dorsolateral striatum (DLS) as mice spontaneously express action sequences. These experiments reveal that DLS neurons systematically encode information about the identity and ordering of sub-second 3D behavioral motifs; this encoding is facilitated by fast-timescale decorrelations between the direct and indirect pathways. Furthermore, lesioning the DLS prevents appropriate sequence assembly during exploratory or odor-evoked behaviors. By characterizing naturalistic behavior at neural timescales, these experiments identify a code for elemental 3D pose dynamics built from complementary pathway dynamics, support a role for DLS in constructing meaningful behavioral sequences, and suggest models for how actions are sculpted over time.

Bayesian nonparametric methods for discovering latent structures of rat hippocampal ensemble spikes

Hippocampal functions are responsible for encoding spatial and temporal dimensions of episodic memory, and hippocampal reactivation of previous awake experiences in sleep is important for learning and memory consolidation. Therefore, uncovering neural representations of hippocampal ensemble spike activity during various behavioral states would provide improved understanding of neural mechanisms of hippocampal-cortical circuits. In this paper, we propose two Bayesian nonparametric methods for this purpose: the Bayesian modeling allows to impose informative priors and constraints into the model, whereas Bayesian nonparametrics allows automatic model selection. We validate these methods to three different hippocampal ensemble recordings under different task behaviors, and provide interpretation and discussion on the derived results.

Comment: A Discussion of “Nonparametric Bayes Modeling of Populations of Networks”

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