@article {2745, title = {Deciphering neural codes of memory during sleep}, journal = {Trends in Neurosciences}, year = {2017}, author = {Zhe Chen and Matthew A. Wilson} } @article {1861, title = {A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation}, journal = {Journal of Neuroscience Methods}, volume = {263}, year = {2016}, type = {Computational Neuroscience}, chapter = {36}, abstract = {

Rodent hippocampal population codes represent important spatial information about the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and propose a 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, to analyze rat hippocampal ensemble spiking activity, 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). We demonstrate the effectiveness of our Bayesian approaches on recordings from a freely-behaving rat navigating in an open field environment. We find that 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.

}, author = {Scott W. Linderman and Matthew J. Johnson and Matthew A. Wilson and Zhe Chen} } @conference {2771, title = {Bayesian nonparametric methods for discovering latent structures of rat hippocampal ensemble spikes}, booktitle = {IEEE Workshop on Machine Learning for Signal Processing}, year = {2016}, month = {09/2016}, address = {Salerno, Italy}, author = {Zhe Chen and Scott W. Linderman and Matthew A. Wilson} } @article {2194, title = {Uncovering representations of sleep-associated hippocampal ensemble spike activity}, journal = {Scientific Reports}, volume = {6}, year = {2016}, month = {08/2016}, abstract = {

Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness.

}, doi = {10.1038/srep32193}, url = {http://dx.doi.org/10.1038/srep32193}, author = {Zhe Chen and Andres D. Grosmark and Hector Penagos and Matthew A. Wilson} } @article {458, title = {A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation.}, number = {027}, year = {2014}, month = {11/2014}, abstract = {

Rodent hippocampal population codes represent important spatial information about the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and propose a 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, to analyze rat hippocampal ensemble spiking activity, 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). We demonstrate the effectiveness of our Bayesian approaches on recordings from a freely-behaving rat navigating in an open field environment. We find that 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.

}, author = {Scott W. Linderman and Matthew J. Johnson and Matthew A. Wilson and Zhe Chen} }