ArticleJournal of neuroscience methods2016
A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation.
Article in Journal of neuroscience methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Drift-diffusion dynamics of hippocampal replay.PLoS computational biology · 2026Article
- A sticky Poisson Hidden Markov Model for solving the problem of over-segmentation and rapid state switching in cortical datasets.bioRxiv : the preprint server for biology · 2025Article
- A sticky Poisson Hidden Markov Model for solving the problem of over-segmentation and rapid state switching in cortical datasets.PloS one · 2025Article
- Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network.Journal of neural engineering · 2024Article
- Active Learning for Discrete Latent Variable Models.Neural computation · 2024Article
- Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network.bioRxiv : the preprint server for biology · 2024Article
- How our understanding of memory replay evolves.Journal of neurophysiology · 2023Review
- Metastable dynamics of neural circuits and networks.Applied physics reviews · 2022Review
- Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials.Cell reports methods · 2021Article
- Article
- Slower prefrontal metastable dynamics during deliberation predicts error trials in a distance discrimination task.Cell reports · 2021Article
- Efficient Position Decoding Methods Based on Fluorescence Calcium Imaging in the Mouse Hippocampus.Neural computation · 2020Article
- Cortical computations via metastable activity.Current opinion in neurobiology · 2019Review
- Dynamics of motor cortical activity during naturalistic feeding behavior.Journal of neural engineering · 2019Article
- Probabilistic Encoding Models for Multivariate Neural Data.Frontiers in neural circuits · 2019Review
- Quantifying Neuronal Information Flow in Response to Frequency and Intensity Changes in the Auditory Cortex.Conference record. Asilomar Conference on Signals, Systems & Computers · 2018Article
- Methods for Assessment of Memory Reactivation.Neural computation · 2018Article
- Gaussian process based nonlinear latent structure discovery in multivariate spike train data.Advances in neural information processing systems · 2017Article
- Deciphering Neural Codes of Memory during Sleep.Trends in neurosciences · 2017Review
- A hidden Markov model for decoding and the analysis of replay in spike trains.Journal of computational neuroscience · 2016Article
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4 authors.
Funding
Abstract
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.
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