@article {5134, title = {Animal-to-Animal Variability in Partial Hippocampal Remapping in Repeated Environments}, journal = {The Journal of Neuroscience}, volume = {42}, year = {2022}, month = {06/2022}, pages = {5268 - 5280}, abstract = {

Hippocampal place cells form a map of the environment of an animal. Changes in the hippocampal map can be brought about in a number of ways, including changes to the environment, task, internal state of the subject, and the passage of time. These changes in the hippocampal map have been called remapping. In this study, we examine remapping during repeated exposure to the same environment. Different animals can have different remapping responses to the same changes. This variability across animals in remapping behavior is not well understood. In this work, we analyzed electrophysiological recordings from the CA3 region of the hippocampus performed by Alme et al. (2014), in which five male rats were exposed to 11 different environments, including a variety of repetitions of those environments. To compare the hippocampal maps between two experiences, we computed average rate map correlation coefficients. We found changes in the hippocampal maps between different sessions in the same environment. These changes consisted of partial remapping, a form of remap- ping in which some place cells maintain their place fields, whereas other place cells remap their place fields. Each animal exhibited partial remapping differently. We discovered that the heterogeneity in hippocampal representational changes across animals is structured; individual animals had consistently different levels of partial remapping across a range of independent comparisons. Our findings highlight that partial hippocampal remapping between repeated environments depends on animal- specific factors.

}, keywords = {context, hippocampus, interindividual variability, overdispersion, place cell, remapping}, issn = {0270-6474}, doi = {10.1523/JNEUROSCI.3221-20.2022}, url = {https://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.3221-20.2022}, author = {Nilchian, Parsa and Matthew A. Wilson and Honi Sanders} } @article {5086, title = {Eszopiclone and Zolpidem Produce Opposite Effects on Hippocampal Ripple DensityDataSheet1.docx}, journal = {Frontiers in Pharmacology}, volume = {12}, year = {2022}, month = {01/2022}, abstract = {

Clinical populations have memory deficits linked to sleep oscillations that can potentially be treated with sleep medications. Eszopiclone and zolpidem (two non-benzodiazepine hypnotics) both enhance sleep spindles. Zolpidem improved sleep-dependent memory consolidation in humans, but eszopiclone did not. These divergent results may reflect that the two drugs have different effects on hippocampal ripple oscillations, which correspond to the reactivation of neuronal ensembles that represent previous waking activity and contribute to memory consolidation. We used extracellular recordings in the CA1 region of rats and systemic dosing of eszopiclone and zolpidem to test the hypothesis that these two drugs differentially affect hippocampal ripples and spike activity. We report evidence that eszopiclone makes ripples sparser, while zolpidem increases ripple density. In addition, eszopiclone led to a drastic decrease in spike firing, both in putative pyramidal cells and interneurons, while zolpidem did not substantially alter spiking. These results provide an explanation of the different effects of eszopiclone and zolpidem on memory in human studies and suggest that sleep medications can be used to regulate hippocampal ripple oscillations, which are causally linked to sleep-dependent memory consolidation.

}, doi = {10.3389/fphar.2021.792148}, url = {https://www.frontiersin.org/articles/10.3389/fphar.2021.792148/full}, author = {Becker, Logan A. and Hector Penagos and Francisco J. Flores and Manoach, Dara S. and Matthew A. Wilson and Varela, Carmen} } @article {4808, title = {Hippocampal remapping as hidden state inference}, journal = {eLife}, volume = {9}, year = {2020}, month = {06/2020}, abstract = {

Cells in the hippocampus tuned to spatial location (place cells) typically change their tuning when an animal changes context, a phenomenon known as remapping. A fundamental challenge to understanding remapping is the fact that what counts as a {\quotesinglbase}{\"A}{\`o}{\quotesinglbase}{\"A}{\`o}context change{\quotesinglbase}{\"A}{\^o}{\quotesinglbase}{\"A}{\^o} has never been precisely defined. Furthermore, different remapping phenomena have been classified on the basis of how much the tuning changes after different types and degrees of context change, but the relationship between these variables is not clear. We address these ambiguities by formalizing remapping in terms of hidden state inference. According to this view, remapping does not directly reflect objective, observable properties of the environment, but rather subjective beliefs about the hidden state of the environment. We show how the hidden state framework can resolve a number of puzzles about the nature of remapping.

}, doi = {10.7554/eLife.51140}, url = {https://elifesciences.org/articles/51140}, author = {Honi Sanders and Matthew A. Wilson and Samuel J Gershman} } @conference {4522, title = {Disruption of CA1 Sharp-Wave Ripples by the nonbenzodiazepine hypnotic eszopiclone }, booktitle = {Society for Neuroscience}, year = {2019}, month = {10/2019}, address = {Chicago, IL, USA}, author = {Becker, LA and Hector Penagos and Manoach, DS and Matthew A. Wilson and Varela, Carmen} } @article {4298, title = {Hippocampal Remapping as Hidden State Inference}, year = {2019}, month = {08/2019}, abstract = {

Cells in the hippocampus tuned to spatial location (place cells) typically change their tuning when an animal changes context, a phenomenon known as remapping. A fundamental challenge to understanding remapping is the fact that what counts as a {\textquotedblleft}context change{\textquotedblright} has never been precisely defined. Furthermore, different remapping phenomena have been classified on the basis of how much the tuning changes after different types and degrees of context change, but the relationship between these variables is not clear. We address these ambiguities by formalizing remapping in terms of hidden state inference. According to this view, remapping does not directly reflect objective, observable properties of the environment, but rather subjective beliefs about the hidden state of the environment. We show how the hidden state framework can resolve a number of puzzles about the nature of remapping.

}, doi = {https://doi.org/10.1101/743260}, author = {Honi Sanders and Matthew A. Wilson and Samuel J Gershman} } @article {3990, title = {Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes}, journal = {Cell Reports}, volume = {25}, year = {2018}, month = {Jan-12-2018}, pages = {2635 - 2642.e5}, abstract = {

Uncovering spatial representations from large-scale ensemble spike activity in specific brain circuits provides valuable feedback in closed-loop experiments. We develop a graphics processing unit (GPU)-powered population-decoding system for ultrafast reconstruction of spatial positions from rodents{\textquoteright} unsorted spatiotemporal spiking patterns, during run behavior or sleep. In comparison with an optimized quad-core central processing unit (CPU) implementation, our approach achieves an \~{}20- to 50-fold increase in speed in eight tested rat hippocampal, cortical, and thalamic ensemble recordings, with real-time decoding speed (approximately fraction of a millisecond per spike) and scalability up to thousands of channels. By accommodating parallel shuffling in real time (computation time \<15 ms), our approach enables assessment of the statistical significance of online-decoded {\textquotedblleft}memory replay{\textquotedblright} candidates during quiet wakefulness or sleep. This open-source software toolkit supports the decoding of spatial correlates or content-triggered experimental manipulation in closed-loop neuroscience experiments.

}, keywords = {GPU, memory replay, neural decoding, place codes, population decoding, spatiotemporal patterns}, issn = {22111247}, doi = {https://doi.org/10.1016/j.celrep.2018.11.033}, url = {https://www.sciencedirect.com/science/article/pii/S2211124718317960}, author = {Hu, Sile and Ciliberti, Davide and Grosmark, Andres D. and Michon, {\'e}d{\'e}ric and Ji, Daoyun and Hector Penagos and {\'a}ki, {\"o}rgy and Matthew A. Wilson and Kloosterman, Fabian and Chen, Zhe} } @article {2745, title = {Deciphering neural codes of memory during sleep}, journal = {Trends in Neurosciences}, year = {2017}, author = {Zhe Chen and Matthew A. Wilson} } @article {2943, title = {Oscillations, neural computations and learning during wake and sleep}, journal = {Current Opinion in Neurobiology}, volume = {44C}, year = {2017}, month = {07/2017}, type = {Review}, chapter = {193}, doi = {https://doi.org/10.1016/j.conb.2017.05.009}, author = {Hector Penagos and Varela, Carmen and Matthew A. Wilson} } @article {3499, title = {Thalamic contribution to CA1-mPFC interactions during sleep}, number = {Program$\#$/Poster$\#$: 799.13/TT8}, year = {2017}, address = {Washington, DC}, abstract = {

The consolidation of episodic memories is thought to require precisely timed interactions between cells in the hippocampus and neocortex during sleep, but the specific mechanisms by which this dialogue unfolds are poorly understood. During sleep, activity in the hippocampus and neocortex is temporally structured by a slow oscillation (1-4Hz) that frames the occurrence of faster oscillations: spindles (7-14Hz) in neocortex, and ripples (150-200Hz) in hippocampus. The observation of spindles suggests the participation of the thalamus, but its contribution has remained an open question. I will present results from simultaneous extracellular recordings of single units and local field potentials in the midline thalamus, mPFC and CA1 in freely behaving rats.

We find that both CA1 ripples and unit firing in the midline thalamus are coordinated with the neocortical slow oscillation. Interestingly, while hippocampal ripples are more likely to occur in 250ms windows before and after neocortical K-complexes (KCs, which mark the downstate of the slow oscillation), spiking probability in a subset of thalamic units is asymmetric and increases following neocortical KCs. Of the units recorded in midline thalamus simultaneously with CA1 and mPFC (n=29), 20.7\% showed a significant increase in firing rate (\>2 standard deviations from baseline) following mPFC KCs. This finding suggests that the time following KCs (the start of the slow oscillation) is functionally different from the end of the oscillation (before KCs), and includes an increased contribution from cells in the midline thalamus, which could influence neocortical populations in preparation for the reactivation of hippocampal memory traces. Furthermore, the correlation between KCs and thalamic units can be modulated by CA1 ripples, suggesting that combined {\textquoteleft}neocortical KC+CA1 ripple{\textquoteright} events can reveal subtle interactions between the three regions. Lastly, units in the reuniens and the ventromedial nuclei show a broad decrease in spiking probability around the time of hippocampal ripples. 57.6\% of units in these nuclei present a significant drop in firing rate compared to 20.8\% in cells recorded in other midline nuclei (p\< 0.05; n=57 units). This suggests that certain thalamic nuclei may be key for gating the transfer of memory information from the hippocampus to neocortex, by opening a time window in which ripples may be more likely to occur.

These results provide the first evidence of the involvement of midline thalamic cells in neocortico-hippocampal interactions during sleep, and point to specific mechanisms by which multi-region brain interactions may contribute to the systems consolidation of memories.

}, author = {Varela, Carmen 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} }