refs.bib

@article{BhattaraiDistincteffectsreward2020,
  title = {Distinct Effects of Reward and Navigation History on Hippocampal Forward and Reverse Replays},
  author = {Bhattarai, Baburam and Lee, Jong Won and Jung, Min Whan},
  year = {2020},
  month = jan,
  volume = {117},
  pages = {689--697},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1912533117},
  abstract = {To better understand the functional roles of hippocampal forward and reverse replays, we trained rats in a spatial sequence memory task and examined how these replays are modulated by reward and navigation history. We found that reward enhances both forward and reverse replays during the awake state, but in different ways. Reward enhances the rate of reverse replays, but it increases the fidelity of forward replays for recently traveled as well as other alternative trajectories heading toward a rewarding location. This suggests roles for forward and reverse replays in reinforcing representations for all potential rewarding trajectories. We also found more faithful reactivation of upcoming than already rewarded trajectories in forward replays. This suggests a role for forward replays in preferentially reinforcing representations for high-value trajectories. We propose that hippocampal forward and reverse replays might contribute to constructing a map of potential navigation trajectories and their associated values (a ``value map'') via distinct mechanisms.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Proceedings of the National Academy of Sciences-/2020/Proceedings of the National Academy of Sciences-2020-Bhattarai et al-Distinct effects of reward and navigation history on hippocampal forward and.pdf},
  journal = {Proceedings of the National Academy of Sciences},
  language = {en},
  number = {1}
}

@article{BrownStatisticalParadigmNeural1998,
  title = {A {{Statistical Paradigm}} for {{Neural Spike Train Decoding Applied}} to {{Position Prediction}} from {{Ensemble Firing Patterns}} of {{Rat Hippocampal Place Cells}}},
  author = {Brown, Emery N. and Frank, Loren M. and Tang, Dengda and Quirk, Michael C. and Wilson, Matthew A.},
  year = {1998},
  month = sep,
  volume = {18},
  pages = {7411--7425},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.18-18-07411.1998},
  file = {/Users/edeno/Dropbox (Personal)/Papers/The Journal of Neuroscience-/1998/The Journal of Neuroscience-1998-Brown et al-A Statistical Paradigm for Neural Spike Train Decoding Applied to Position.pdf},
  journal = {The Journal of Neuroscience},
  language = {en},
  number = {18}
}

@article{CareyRewardrevaluationbiases2019,
  title = {Reward Revaluation Biases Hippocampal Replay Content Away from the Preferred Outcome},
  author = {Carey, Alyssa A. and Tanaka, Youki and {van der Meer}, Matthijs A. A.},
  year = {2019},
  month = aug,
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/s41593-019-0464-6},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Nature Neuroscience-/2019/Nature Neuroscience-2019-Carey et al-Reward revaluation biases hippocampal replay content away from the preferred.pdf;/Users/edeno/Dropbox (Personal)/Papers/Nature Neuroscience-/2019/Nature Neuroscience-2019-Carey et al-Reward revaluation biases hippocampal replay content away from the preferred2.pdf},
  journal = {Nature Neuroscience},
  language = {en}
}

@article{CarrHippocampalreplayawake2011,
  title = {Hippocampal Replay in the Awake State: A Potential Substrate for Memory Consolidation and Retrieval},
  shorttitle = {Hippocampal Replay in the Awake State},
  author = {Carr, Margaret F and Jadhav, Shantanu P and Frank, Loren M},
  year = {2011},
  month = feb,
  volume = {14},
  pages = {147--153},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn.2732},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Nature Neuroscience-/2011/Nature Neuroscience-2011-Carr et al-Hippocampal replay in the awake state.pdf},
  journal = {Nature Neuroscience},
  number = {2}
}

@article{CarrTransientSlowGamma2012,
  title = {Transient {{Slow Gamma Synchrony Underlies Hippocampal Memory Replay}}},
  author = {Carr, Margaret F. and Karlsson, Mattias P. and Frank, Loren M.},
  year = {2012},
  month = aug,
  volume = {75},
  pages = {700--713},
  issn = {08966273},
  doi = {10.1016/j.neuron.2012.06.014},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2012/Neuron-2012-Carr et al-Transient Slow Gamma Synchrony Underlies Hippocampal Memory Replay.pdf},
  journal = {Neuron},
  language = {en},
  number = {4}
}

@book{CasellaStatisticalinference2001,
  title = {Statistical Inference},
  author = {Casella, George and Berger, Roger L.},
  year = {2001},
  volume = {2},
  publisher = {{Duxbury Pacific Grove, CA}},
  file = {/Users/edeno/Dropbox (Personal)/Papers/undefined/Casella_Berger-Statistical inference.pdf},
  isbn = {0-534-24312-6 978-0-534-24312-8},
  series = {Duxbury Advanced Series in Statistics and Decision Sciences}
}

@inproceedings{ChenBayesiannonparametricmethods2016,
  title = {Bayesian Nonparametric Methods for Discovering Latent Structures of Rat Hippocampal Ensemble Spikes},
  booktitle = {2016 {{IEEE}} 26th {{International Workshop}} on {{Machine Learning}} for {{Signal Processing}} ({{MLSP}})},
  author = {Chen, Zhe and Linderman, Scott W. and Wilson, Matthew A.},
  year = {2016},
  month = sep,
  pages = {1--6},
  publisher = {{IEEE}},
  address = {{Vietri sul Mare, Salerno, Italy}},
  doi = {10.1109/MLSP.2016.7738867},
  abstract = {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.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)-/2016/2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)-2016-Chen et al-Bayesian nonparametric methods for discovering latent structures of rat.pdf},
  isbn = {978-1-5090-0746-2},
  language = {en}
}

@article{ChengNewExperiencesEnhance2008,
  title = {New {{Experiences Enhance Coordinated Neural Activity}} in the {{Hippocampus}}},
  author = {Cheng, Sen and Frank, Loren M.},
  year = {2008},
  month = jan,
  volume = {57},
  pages = {303--313},
  issn = {08966273},
  doi = {10.1016/j.neuron.2007.11.035},
  abstract = {The acquisition of new memories for places and events requires synaptic plasticity in the hippocampus, and plasticity depends on temporal coordination among neurons. Spatial activity in the hippocampus is relatively disorganized during the initial exploration of a novel environment, however, and it is unclear how neural activity during the initial stages of learning drives synaptic plasticity. Here we show that pairs of CA1 cells that represent overlapping novel locations are initially more coactive and more precisely coordinated than are cells representing overlapping familiar locations. This increased coordination occurrs specifically during brief, high-frequency events (HFEs) in the local field potential that are similar to ripples and is not associated with better coordination of place-specific neural activity outside of HFEs. As novel locations become more familiar, correlations between cell pairs decrease. Thus, hippocampal neural activity during learning has a unique structure that is well suited to induce synaptic plasticity and to allow for rapid storage of new memories.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2008/Neuron-2008-Cheng_Frank-New Experiences Enhance Coordinated Neural Activity in the Hippocampus.pdf},
  journal = {Neuron},
  language = {en},
  number = {2}
}

@inproceedings{ChenTransductiveneuraldecoding2012,
  title = {Transductive Neural Decoding for Unsorted Neuronal Spikes of Rat Hippocampus},
  booktitle = {Engineering in {{Medicine}} and {{Biology Society}} ({{EMBC}}), 2012 {{Annual International Conference}} of the {{IEEE}}},
  author = {Chen, Zhe and Kloosterman, Fabian and Layton, Stuart and Wilson, Matthew A.},
  year = {2012},
  pages = {1310--1313},
  publisher = {{IEEE}},
  doi = {10.1109/EMBC.2012.6346178},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE-/2012/Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE-2012-Chen et al-Transductive neural decoding for unsorted neuronal spikes of rat hippocampus.pdf}
}

@article{ChenUncoveringspatialtopology2012,
  title = {Uncovering Spatial Topology Represented by Rat Hippocampal Population Neuronal Codes},
  author = {Chen, Zhe and Kloosterman, Fabian and Brown, Emery N. and Wilson, Matthew A.},
  year = {2012},
  month = oct,
  volume = {33},
  pages = {227--255},
  issn = {0929-5313, 1573-6873},
  doi = {10.1007/s10827-012-0384-x},
  abstract = {Hippocampal population codes play an important role in representation of spatial environment and spatial navigation. Uncovering the internal representation of hippocampal population codes will help understand neural mechanisms of the hippocampus. For instance, uncovering the patterns represented by rat hippocampus (CA1) pyramidal cells during periods of either navigation or sleep has been an active research topic over the past decades. However, previous approaches to analyze or decode firing patterns of population neurons all assume the knowledge of the place fields, which are estimated from training data a priori. The question still remains unclear how can we extract information from population neuronal responses either without a priori knowledge or in the presence of finite sampling constraint. Finding the answer to this question would leverage our ability to examine the population neuronal codes under different experimental conditions. Using rat hippocampus as a model system, we attempt to uncover the hidden ``spatial topology'' represented by the hippocampal population codes. We develop a hidden Markov model (HMM) and a variational Bayesian (VB) inference algorithm to achieve this computational goal, and we apply the analysis to extensive simulation and experimental data. Our empirical results show promising direction for discovering structural patterns of ensemble spike activity during periods of active navigation. This study would also provide useful insights for future exploratory data analysis of population neuronal codes during periods of sleep.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Journal of Computational Neuroscience-/2012/Journal of Computational Neuroscience-2012-Chen et al-Uncovering spatial topology represented by rat hippocampal population neuronal.pdf},
  journal = {Journal of Computational Neuroscience},
  language = {en},
  number = {2}
}

@article{ChungFullyAutomatedApproach2017,
  title = {A {{Fully Automated Approach}} to {{Spike Sorting}}},
  author = {Chung, Jason E. and Magland, Jeremy F. and Barnett, Alex H. and Tolosa, Vanessa M. and Tooker, Angela C. and Lee, Kye Y. and Shah, Kedar G. and Felix, Sarah H. and Frank, Loren M. and Greengard, Leslie F.},
  year = {2017},
  month = sep,
  volume = {95},
  pages = {1381-1394.e6},
  issn = {08966273},
  doi = {10.1016/j.neuron.2017.08.030},
  abstract = {Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2017/Neuron-2017-Chung et al-A Fully Automated Approach to Spike Sorting.pdf},
  journal = {Neuron},
  language = {en},
  number = {6}
}

@article{DavidsonHippocampalReplayExtended2009,
  title = {Hippocampal {{Replay}} of {{Extended Experience}}},
  author = {Davidson, Thomas J. and Kloosterman, Fabian and Wilson, Matthew A.},
  year = {2009},
  month = aug,
  volume = {63},
  pages = {497--507},
  issn = {08966273},
  doi = {10.1016/j.neuron.2009.07.027},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2009/Neuron-2009-Davidson et al-Hippocampal Replay of Extended Experience.pdf},
  journal = {Neuron},
  language = {en},
  number = {4}
}

@article{DengClusterlessDecodingPosition2015,
  title = {Clusterless {{Decoding}} of {{Position}} from {{Multiunit Activity Using}} a {{Marked Point Process Filter}}},
  author = {Deng, Xinyi and Liu, Daniel F. and Kay, Kenneth and Frank, Loren M. and Eden, Uri T.},
  year = {2015},
  month = jul,
  volume = {27},
  pages = {1438--1460},
  issn = {0899-7667, 1530-888X},
  doi = {10.1162/NECO_a_00744},
  abstract = {Point process filters have been applied successfully to decode neural signals and track neural dynamics. Traditionally, these methods assume that multiunit spiking activity has already been correctly spike-sorted. As a result, these methods are not appropriate for situations where sorting cannot be performed with high precision such as real-time decoding for brain-computer interfaces. As the unsupervised spike-sorting problem remains unsolved, we took an alternative approach that takes advantage of recent insights about clusterless decoding. Here we present a new point process decoding algorithm that does not require multiunit signals to be sorted into individual units. We use the theory of marked point processes to construct a function that characterizes the relationship between a covariate of interest (in this case, the location of a rat on a track) and features of the spike waveforms. In our example, we use tetrode recordings, and the marks represent a fourdimensional vector of the maximum amplitudes of the spike waveform on each of the four electrodes. In general, the marks may represent any features of the spike waveform. We then use Bayes' rule to estimate spatial location from hippocampal neural activity.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neural Computation-/2015/Neural Computation-2015-Deng et al-Clusterless Decoding of Position from Multiunit Activity Using a Marked Point.pdf},
  journal = {Neural Computation},
  language = {en},
  number = {7}
}

@article{DengRapidclassificationhippocampal2016,
  title = {Rapid Classification of Hippocampal Replay Content for Real-Time Applications},
  author = {Deng, Xinyi and Liu, Daniel F. and Karlsson, Mattias P. and Frank, Loren M. and Eden, Uri T.},
  year = {2016},
  month = nov,
  volume = {116},
  pages = {2221--2235},
  issn = {0022-3077, 1522-1598},
  doi = {10.1152/jn.00151.2016},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Journal of Neurophysiology-/2016/Journal of Neurophysiology-2016-Deng et al-Rapid classification of hippocampal replay content for real-time applications.pdf},
  journal = {Journal of Neurophysiology},
  language = {en},
  number = {5}
}

@inproceedings{DenovellisCharacterizinghippocampalreplay2019,
  title = {Characterizing Hippocampal Replay Using Hybrid Point Process State Space Models},
  booktitle = {2019 53rd {{Asilomar Conference}} on {{Signals}}, {{Systems}}, and {{Computers}}},
  author = {Denovellis, Eric L. and Frank, Loren M. and Eden, Uri T.},
  year = {2019},
  month = nov,
  pages = {245--249},
  publisher = {{IEEE}},
  address = {{Pacific Grove, CA, USA}},
  doi = {10.1109/IEEECONF44664.2019.9048688},
  abstract = {In the hippocampus, replay sequences are temporally compressed patterns of neural spiking that resemble patterns that occur when the animal is moving through the environment. Because replay sequences typically occur when the animal is at rest, replay is hypothesized to be part of an internal cognitive process that enables the retrieval of past spatial memories and the planning of future movement. Traditionally, replay sequences have been discovered by identifying sharp wave ripples (SWRs)\textemdash high frequency oscillations that occur in association with replay\textemdash and then looking within SWRs for spatially continuous patterns of neural spiking. This does not fully account for the content or timing of replay sequences, however. Replay sequences do not always co-occur with sharp wave ripples, have more complex dynamics than spatially continuous movement, have different temporal ordering than during movement, and change based on task. In this work, we introduce a hybrid state space framework to describe the richness of replay sequences. We show how defining discrete latent states associated with continuous latent dynamics and point process observations allows us to identify when non-local replay sequences occur, categorize the type of sequence based on their inferred continuous dynamics, and decode the spatial trajectory corresponding to the replay sequence.},
  copyright = {All rights reserved},
  file = {/Users/edeno/Dropbox (Personal)/Papers/2019 53rd Asilomar Conference on Signals, Systems, and Computers-/2019/2019 53rd Asilomar Conference on Signals, Systems, and Computers-2019-Denovellis et al-Characterizing hippocampal replay using hybrid point process state space models.pdf},
  isbn = {978-1-72814-300-2},
  language = {en}
}

@article{DibaForwardreversehippocampal2007a,
  title = {Forward and Reverse Hippocampal Place-Cell Sequences during Ripples},
  author = {Diba, Kamran and Buzs{\'a}ki, Gy{\"o}rgy},
  year = {2007},
  month = oct,
  volume = {10},
  pages = {1241--1242},
  issn = {1546-1726},
  doi = {10.1038/nn1961},
  abstract = {We report that temporal spike sequences from hippocampal place neurons of rats on an elevated track recurred in reverse order at the end of a run, but in forward order in anticipation of the run, coinciding with sharp waves. Vector distances between the place fields were reflected in the temporal structure of these sequences. This bidirectional re-enactment of temporal sequences may contribute to the establishment of higher-order associations in episodic memory.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Nature Neuroscience-/2007/Nature Neuroscience-2007-Diba_Buzsáki-Forward and reverse hippocampal place-cell sequences during ripples.pdf},
  journal = {Nature Neuroscience},
  number = {10}
}

@article{Dijkstranotetwoproblems1959,
  title = {A Note on Two Problems in Connexion with Graphs},
  author = {Dijkstra, Edsger W.},
  year = {1959},
  volume = {1},
  pages = {269--271},
  doi = {10.1007/BF01386390},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Numerische mathematik-/1959/Numerische mathematik-1959-Dijkstra-A note on two problems in connexion with graphs.pdf},
  isbn = {0029-599X},
  journal = {Numerische mathematik},
  number = {1}
}

@article{DrieuNestedsequenceshippocampal2018,
  ids = {DrieuNestedsequenceshippocampal2018a},
  title = {Nested Sequences of Hippocampal Assemblies during Behavior Support Subsequent Sleep Replay},
  author = {Drieu, C{\'e}line and Todorova, Ralitsa and Zugaro, Micha{\"e}l},
  year = {2018},
  month = nov,
  volume = {362},
  pages = {675},
  publisher = {{American Association for the Advancement of Science}},
  doi = {10.1126/science.aat2952},
  abstract = {Hippocampal replay of place cell sequences during sleep is critical for memory consolidation in target cortical areas. How is the sequential organization of place cell assemblies maintained across different time scales? Drieu et al. compared periods when a rat either sat passively on a moving train or ran actively on a treadmill on the same train. During the passive movement, the slow behavioral sequence of place cells was still present, but the rapid generation of theta sequences was lost. Active running on the treadmill, however, maintained the theta rhythm. After passive transport, sequence replay during sleep was destroyed, whereas active running protected replay.Science, this issue p. 675Consolidation of spatial and episodic memories is thought to rely on replay of neuronal activity sequences during sleep. However, the network dynamics underlying the initial storage of memories during wakefulness have never been tested. Although slow, behavioral time scale sequences have been claimed to sustain sequential memory formation, fast (``theta'') time scale sequences, nested within slow sequences, could be instrumental. We found that in rats traveling passively on a model train, place cells formed behavioral time scale sequences but theta sequences were degraded, resulting in impaired subsequent sleep replay. In contrast, when the rats actively ran on a treadmill while being transported on the train, place cells generated clear theta sequences and accurate trajectory replay during sleep. Our results support the view that nested sequences underlie the initial formation of memory traces subsequently consolidated during sleep.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/undefined/2018/2018-Drieu et al-Nested sequences of hippocampal assemblies during behavior support subsequent.pdf},
  journal = {Science},
  number = {6415}
}

@book{EichenbaumConditioningConsciousRecollection2004,
  ids = {Eichenbaumconditioningconsciousrecollection2004},
  title = {From {{Conditioning}} to {{Conscious Recollection}}: {{Memory}} Systems of the Brain},
  author = {Eichenbaum, Howard and Cohen, Neal J.},
  year = {2004},
  publisher = {{Oxford University Press}},
  address = {{New York}},
  doi = {10.1093/acprof:oso/9780195178043.001.0001},
  abstract = {This book provides a comprehensive treatment of the history and implications of the notion of multiple memory systems, of the evidence that supports it, and of the nature of the systems discovered so far. The book begins by highlighting a brief history of ideas about multiple memory systems and how those ideas fit into the story of the progression of our understanding of the nature and organization of memory in the brain. Other early chapters address some of the themes and principles that are common to all memory systems, including the fundamentals of cellular plasticity and the critical role of the cerebral cortex in memory. The central portion of the book then attempts to characterize the role of several specific memory systems, starting with a detailed analysis of the hippocampal memory system \textemdash{} the brain system that mediates declarative memory, our ability to recollect consciously everyday facts and experiences, by supporting the capacity for relational memory processing. Individual chapters focus on non-human primate and rodent models of amnesia, on hippocampal neuronal activity, and on the permanent consolidation of declarative memories. Subsequent chapters present evidence of functional dissociations among various memory systems. These chapters identify and describe brain systems that mediate emotional memories, modulate memory, or mediate the acquisition of behavioral habits (procedural memory), all concerned with long-term memory abilities, and a system focused on the prefrontal cortex that supports working memory.},
  isbn = {978-0-19-517804-3},
  keywords = {amnesia,cerebral cortex,conscious recollection,consolidation,declarative memory,emotional memory,ER,multiple memory systems,plasticity,procedural memory,relational memory},
  language = {eng},
  number = {35},
  series = {Oxford {{Psychology Series}}}
}

@article{FarooqEmergencepreconfiguredplastic2019,
  title = {Emergence of Preconfigured and Plastic Time-Compressed Sequences in Early Postnatal Development},
  author = {Farooq, U. and Dragoi, G.},
  year = {2019},
  month = jan,
  volume = {363},
  pages = {168--173},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.aav0502},
  abstract = {When and how hippocampal neuronal ensembles first organize to support encoding and consolidation of memory episodes, a critical cognitive function of the brain, are unknown. We recorded electrophysiological activity from large ensembles of hippocampal neurons starting on the first day after eye opening as na\"ive rats navigated linear environments and slept. We found a gradual age-dependent, navigational experience\textendash independent assembly of preconfigured trajectory-like sequences from persistent, location-depicting ensembles during postnatal week 3. Adult-like compressed binding of adjacent locations into trajectories during navigation and their navigational experience\textendash dependent replay during sleep emerged in concert from spontaneous preconfigured sequences only during early postnatal week 4. Our findings reveal ethologically relevant distinct phases in the development of hippocampal preconfigured and experience-dependent sequential patterns thought to be important for episodic memory formation.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Science-/2019/Science-2019-Farooq_Dragoi-Emergence of preconfigured and plastic time-compressed sequences in early.pdf},
  journal = {Science},
  language = {en},
  number = {6423}
}

@article{FarooqStrengthenedTemporalCoordination2019,
  title = {Strengthened {{Temporal Coordination}} within {{Pre}}-Existing {{Sequential Cell Assemblies Supports Trajectory Replay}}},
  author = {Farooq, Usman and Sibille, Jeremie and Liu, Kefei and Dragoi, George},
  year = {2019},
  month = aug,
  volume = {103},
  pages = {719-733.e7},
  issn = {08966273},
  doi = {10.1016/j.neuron.2019.05.040},
  abstract = {A central goal in learning and memory research is to reveal the neural substrates underlying episodic memory formation. The hallmark of sequential spatial trajectory learning, a model of episodic memory, has remained equivocal, with proposals ranging from de novo creation of compressed sequential replay from blank slate networks to selection of pre-existing compressed preplay sequences. Here, we show that increased millisecond-timescale activation of cell assemblies expressed during de novo sequential experience and increased neuronal firing rate correlations can explain the difference between postexperience trajectory replay and robust preplay. This increased activation results from an improved neuronal tuning to specific cell assemblies, higher recruitment of experience-tuned neurons into preexisting cell assemblies, and increased recruitment of cell assemblies in replay. In contrast, changes in overall neuronal and cell assembly temporal order within extended sequences do not account for sequential trajectory learning. We propose the coordinated strengthening of cell assemblies played sequentially on robust pre-existing temporal frameworks could support rapid formation of episodiclike memory.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2019/Neuron-2019-Farooq et al-Strengthened Temporal Coordination within Pre-existing Sequential Cell.pdf},
  journal = {Neuron},
  language = {en},
  number = {4}
}

@article{FosterReversereplaybehavioural2006,
  title = {Reverse Replay of Behavioural Sequences in Hippocampal Place Cells during the Awake State},
  author = {Foster, David J. and Wilson, Matthew A.},
  year = {2006},
  month = mar,
  volume = {440},
  pages = {680--683},
  issn = {0028-0836, 1476-4679},
  doi = {10.1038/nature04587},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Nature-/2006/Nature-2006-Foster_Wilson-Reverse replay of behavioural sequences in hippocampal place cells during the.pdf},
  journal = {Nature},
  number = {7084}
}

@article{FoxLocalizationanatomicalidentification1975,
  title = {Localization and Anatomical Identification of Theta and Complex Spike Cells in Dorsal Hippocampal Formation of Rats},
  author = {Fox, So E and Ranck Jr, James B},
  year = {1975},
  volume = {49},
  pages = {299--313},
  publisher = {{Elsevier}},
  issn = {0014-4886},
  doi = {10.1016/0014-4886(75)90213-7},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Experimental neurology-/1975/Experimental neurology-1975-Fox_Ranck Jr-Localization and anatomical identification of theta and complex spike cells in.pdf},
  journal = {Experimental Neurology},
  number = {1}
}

@article{Franklandorganizationrecentremote2005,
  title = {The Organization of Recent and Remote Memories},
  author = {Frankland, Paul W. and Bontempi, Bruno},
  year = {2005},
  month = feb,
  volume = {6},
  pages = {119--130},
  issn = {1471-003X, 1471-0048},
  doi = {10.1038/nrn1607},
  abstract = {A fundamental question in memory research is how our brains can form enduring memories. In humans, memories of everyday life depend initially on the medial temporal lobe system, including the hippocampus. As these memories mature, they are thought to become increasingly dependent on other brain regions such as the cortex. Little is understood about how new memories in the hippocampus are transformed into remote memories in cortical networks. However, recent studies have begun to shed light on how remote memories are organized in the cortex, and the molecular and cellular events that underlie their consolidation.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Nature Reviews Neuroscience-/2005/Nature Reviews Neuroscience-2005-Frankland_Bontempi-The organization of recent and remote memories.pdf},
  journal = {Nature Reviews Neuroscience},
  language = {en},
  number = {2}
}

@article{GrosmarkDiversityneuralfiring2016,
  title = {Diversity in Neural Firing Dynamics Supports Both Rigid and Learned Hippocampal Sequences},
  author = {Grosmark, A. D. and Buzs{\'a}ki, G.},
  year = {2016},
  month = mar,
  volume = {351},
  pages = {1440--1443},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.aad1935},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Science-/2016/Science-2016-Grosmark_Buzsaki-Diversity in neural firing dynamics supports both rigid and learned hippocampal.pdf},
  journal = {Science},
  language = {en},
  number = {6280}
}

@article{GuptaHippocampalReplayNot2010,
  title = {Hippocampal {{Replay Is Not}} a {{Simple Function}} of {{Experience}}},
  author = {Gupta, Anoopum S. and {van der Meer}, Matthijs A.A. and Touretzky, David S. and Redish, A. David},
  year = {2010},
  month = mar,
  volume = {65},
  pages = {695--705},
  issn = {08966273},
  doi = {10.1016/j.neuron.2010.01.034},
  abstract = {Replay of behavioral sequences in the hippocampus during sharp wave ripple complexes (SWRs) provides a potential mechanism for memory consolidation and the learning of knowledge structures. Current hypotheses imply that replay should straightforwardly reflect recent experience. However, we find these hypotheses to be incompatible with the content of replay on a task with two distinct behavioral sequences (A and B). We observed forward and backward replay of B even when rats had been performing A for {$>$}10 min. Furthermore, replay of nonlocal sequence B occurred more often when B was infrequently experienced. Neither forward nor backward sequences preferentially represented highly experienced trajectories within a session. Additionally, we observed the construction of never-experienced novel-path sequences. These observations challenge the idea that sequence activation during SWRs is a simple replay of recent experience. Instead, replay reflected all physically available trajectories within the environment, suggesting a potential role in active learning and maintenance of the cognitive map.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2010/Neuron-2010-Gupta et al-Hippocampal Replay Is Not a Simple Function of Experience.pdf},
  journal = {Neuron},
  language = {en},
  number = {5}
}

@inproceedings{HagbergExploringNetworkStructure2008,
  title = {Exploring {{Network Structure}}, {{Dynamics}}, and {{Function}} Using {{NetworkX}}},
  booktitle = {Proceedings of the 7th {{Python}} in {{Science Conference}}},
  author = {Hagberg, Aric A and Schult, Daniel A and Swart, Pieter J},
  year = {2008},
  pages = {11--15},
  address = {{Pasadena, CA USA}},
  file = {/Users/edeno/Dropbox (Personal)/Papers/undefined/2008/2008-Hagberg et al-Exploring Network Structure, Dynamics, and Function using NetworkX.pdf},
  language = {en}
}

@article{HangyaComplementaryspatialfiring2010,
  title = {Complementary Spatial Firing in Place Cell-Interneuron Pairs: {{Place}} Cell-Interneuron Spatial Correlation},
  shorttitle = {Complementary Spatial Firing in Place Cell-Interneuron Pairs},
  author = {Hangya, Bal{\'a}zs and Li, Yu and Muller, Robert U. and Czurk{\'o}, Andr{\'a}s},
  year = {2010},
  month = nov,
  volume = {588},
  pages = {4165--4175},
  issn = {00223751},
  doi = {10.1113/jphysiol.2010.194274},
  file = {/Users/edeno/Dropbox (Personal)/Papers/The Journal of Physiology-/2010/The Journal of Physiology-2010-Hangya et al-Complementary spatial firing in place cell-interneuron pairs.pdf},
  journal = {The Journal of Physiology},
  language = {en},
  number = {21}
}

@article{HarrisArrayprogrammingNumPy2020,
  ids = {HarrisArrayprogrammingNumPy},
  title = {Array Programming with {{NumPy}}},
  author = {Harris, Charles R. and Millman, K. Jarrod and {van der Walt}, St{\'e}fan J. and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J. and Kern, Robert and Picus, Matti and Hoyer, Stephan and {van Kerkwijk}, Marten H. and Brett, Matthew and Haldane, Allan and {del R{\'i}o}, Jaime Fern{\'a}ndez and Wiebe, Mark and Peterson, Pearu and {G{\'e}rard-Marchant}, Pierre and Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and Abbasi, Hameer and Gohlke, Christoph and Oliphant, Travis E.},
  year = {2020},
  month = sep,
  volume = {585},
  pages = {357--362},
  issn = {1476-4687},
  doi = {10.1038/s41586-020-2649-2},
  abstract = {Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/undefined/undefined/Harris-Array programming with NumPy.pdf},
  journal = {Nature},
  number = {7825}
}

@article{HarrisTemporalInteractionSingle2001,
  title = {Temporal {{Interaction}} between {{Single Spikes}} and {{Complex Spike Bursts}} in {{Hippocampal Pyramidal Cells}}},
  author = {Harris, Kenneth D and Hirase, Hajime and Leinekugel, Xavier and Henze, Darrell A and Buzs{\'a}ki, Gy{\"o}rgy},
  year = {2001},
  month = oct,
  volume = {32},
  pages = {141--149},
  issn = {08966273},
  doi = {10.1016/S0896-6273(01)00447-0},
  abstract = {Cortical pyramidal cells fire single spikes and complex spike bursts. However, neither the conditions necessary for triggering complex spikes, nor their computational function are well understood. CA1 pyramidal cell burst activity was examined in behaving rats. The fraction of bursts was not reliably higher in place field centers, but rather in places where discharge frequency was 6\textendash 7 Hz. Burst probability was lower and bursts were shorter after recent spiking activity than after prolonged periods of silence (100 ms\textendash 1 s). Burst initiation probability and burst length were correlated with extracellular spike amplitude and with intracellular action potential rising slope. We suggest that bursts may function as ``conditional synchrony detectors,'' signaling strong afferent synchrony after neuronal silence, and that single spikes triggered by a weak input may suppress bursts evoked by a subsequent strong input.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2001/Neuron-2001-Harris et al-Temporal Interaction between Single Spikes and Complex Spike Bursts in.pdf},
  journal = {Neuron},
  language = {en},
  number = {1}
}

@article{HoyerxarrayNDlabeled2017,
  title = {Xarray: {{N}}-{{D}} Labeled {{Arrays}} and {{Datasets}} in {{Python}}},
  shorttitle = {Xarray},
  author = {Hoyer, Stephan and Hamman, Joseph J.},
  year = {2017},
  month = apr,
  volume = {5},
  pages = {10},
  issn = {2049-9647},
  doi = {10.5334/jors.148},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Journal of Open Research Software-/2017/Journal of Open Research Software-2017-Hoyer_Hamman-xarray.pdf},
  journal = {Journal of Open Research Software},
  language = {en}
}

@article{HunterMatplotlib2DGraphics2007,
  title = {Matplotlib: {{A 2D Graphics Environment}}},
  shorttitle = {Matplotlib},
  author = {Hunter, John D.},
  year = {2007},
  volume = {9},
  pages = {90--95},
  issn = {1521-9615},
  doi = {10.1109/MCSE.2007.55},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Computing in Science & Engineering-/2007/Computing in Science & Engineering-2007-Hunter-Matplotlib.pdf},
  journal = {Computing in Science \& Engineering},
  language = {en},
  number = {3}
}

@article{JadhavCoordinatedExcitationInhibition2016,
  title = {Coordinated {{Excitation}} and {{Inhibition}} of {{Prefrontal Ensembles}} during {{Awake Hippocampal Sharp}}-{{Wave Ripple Events}}},
  author = {Jadhav, Shantanu P. and Rothschild, Gideon and Roumis, Demetris K. and Frank, Loren M.},
  year = {2016},
  month = apr,
  volume = {90},
  pages = {113--127},
  issn = {08966273},
  doi = {10.1016/j.neuron.2016.02.010},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2016/Neuron-2016-Jadhav et al-Coordinated Excitation and Inhibition of Prefrontal Ensembles during Awake.pdf},
  journal = {Neuron},
  language = {en},
  number = {1}
}

@article{JinnoNeuronalDiversityGABAergic2007,
  title = {Neuronal {{Diversity}} in {{GABAergic Long}}-{{Range Projections}} from the {{Hippocampus}}},
  author = {Jinno, S. and Klausberger, T. and Marton, L. F. and Dalezios, Y. and Roberts, J. D. B. and Fuentealba, P. and Bushong, E. A. and Henze, D. and Buzsaki, G. and Somogyi, P.},
  year = {2007},
  month = aug,
  volume = {27},
  pages = {8790--8804},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.1847-07.2007},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Journal of Neuroscience-/2007/Journal of Neuroscience-2007-Jinno et al-Neuronal Diversity in GABAergic Long-Range Projections from the Hippocampus.pdf},
  journal = {Journal of Neuroscience},
  language = {en},
  number = {33}
}

@incollection{JohnsonMeasuringdistributedproperties2008,
  title = {Measuring Distributed Properties of Neural Representations beyond the Decoding of Local Variables: Implications for Cognition},
  shorttitle = {Measuring Distributed Properties of Neural Representations beyond the Decoding of Local Variables},
  booktitle = {Information {{Processing}} by {{Neuronal Populations}}},
  author = {Johnson, Adam and Jackson, Jadin C. and Redish, A. David},
  editor = {Holscher, Christian and Munk, Matthias},
  year = {2008},
  pages = {95--119},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge}},
  doi = {10.1017/CBO9780511541650.005},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Information Processing by Neuronal Populations-/2008/Information Processing by Neuronal Populations-2008-Johnson et al-Measuring distributed properties of neural representations beyond the decoding.pdf},
  isbn = {978-0-511-54165-0 978-0-521-87303-1},
  language = {en}
}

@article{Joohippocampalsharpwave2018,
  title = {The Hippocampal Sharp Wave\textendash Ripple in Memory Retrieval for Immediate Use and Consolidation},
  author = {Joo, Hannah R. and Frank, Loren M.},
  year = {2018},
  month = dec,
  volume = {19},
  pages = {744--757},
  issn = {1471-003X, 1471-0048},
  doi = {10.1038/s41583-018-0077-1},
  abstract = {Various cognitive functions have long been known to require the hippocampus. Recently{$\mkern1mu$}, progress has been made in identifying the hippocampal neural activity patterns that implement these functions. One such pattern is the sharp wave\textendash ripple (SWR), an event associated with highly synchronous neural firing in the hippocampus and modulation of neural activity in distributed brain regions. Hippocampal spiking during SWRs can represent past or potential future experience, and SWR-related interventions can alter subsequent memory performance. These findings and others suggest that SWRs support both memory consolidation and memory retrieval for processes such as decision-making. In addition, studies have identified distinct types of SWR based on representational content, behavioural state and physiological features. These various findings regarding SWRs suggest that different SWR types correspond to different cognitive functions, such as retrieval and consolidation. Here, we introduce another possibility \textemdash{} that a single SWR may support more than one cognitive function. Taking into account classic psychological theories and recent molecular results that suggest that retrieval and consolidation share mechanisms, we propose that the SWR mediates the retrieval of stored representations that can be utilized immediately by downstream circuits in decision-making, planning, recollection and/or imagination while simultaneously initiating memory consolidation processes.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Nature Reviews Neuroscience-/2018/Nature Reviews Neuroscience-2018-Joo_Frank-The hippocampal sharp wave–ripple in memory retrieval for immediate use and.pdf},
  journal = {Nature Reviews Neuroscience},
  language = {en},
  number = {12}
}

@article{KaeferReplayBehavioralSequences2020,
  title = {Replay of {{Behavioral Sequences}} in the {{Medial Prefrontal Cortex}} during {{Rule Switching}}},
  author = {Kaefer, Karola and Nardin, Michele and Blahna, Karel and Csicsvari, Jozsef},
  year = {2020},
  month = feb,
  pages = {S0896627320300416},
  issn = {08966273},
  doi = {10.1016/j.neuron.2020.01.015},
  abstract = {Temporally organized reactivation of experiences during awake immobility periods is thought to underlie cognitive processes like planning and evaluation. While replay of trajectories is well established for the hippocampus, it is unclear whether the medial prefrontal cortex (mPFC) can reactivate sequential behavioral experiences in the awake state to support task execution. We simultaneously recorded from hippocampal and mPFC principal neurons in rats performing a mPFC-dependent rule-switching task on a plus maze. We found that mPFC neuronal activity encoded relative positions between the start and goal. During awake immobility periods, the mPFC replayed temporally organized sequences of these generalized positions, resembling entire spatial trajectories. The occurrence of mPFC trajectory replay positively correlated with rule-switching performance. However, hippocampal and mPFC trajectory replay occurred independently, indicating different functions. These results demonstrate that the mPFC can replay ordered activity patterns representing generalized locations and suggest that mPFC replay might have a role in flexible behavior.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2020/Neuron-2020-Kaefer et al-Replay of Behavioral Sequences in the Medial Prefrontal Cortex during Rule.pdf},
  journal = {Neuron},
  language = {en}
}

@article{KarlssonAwakereplayremote2009,
  title = {Awake Replay of Remote Experiences in the Hippocampus},
  author = {Karlsson, Mattias P and Frank, Loren M},
  year = {2009},
  month = jul,
  volume = {12},
  pages = {913--918},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn.2344},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Nature Neuroscience-/2009/Nature Neuroscience-2009-Karlsson_Frank-Awake replay of remote experiences in the hippocampus.pdf},
  journal = {Nature Neuroscience},
  number = {7}
}

@article{KayConstantSubsecondCycling2020,
  title = {Constant {{Sub}}-Second {{Cycling}} between {{Representations}} of {{Possible Futures}} in the {{Hippocampus}}},
  author = {Kay, Kenneth and Chung, Jason E. and Sosa, Marielena and Schor, Jonathan S. and Karlsson, Mattias P. and Larkin, Margaret C. and Liu, Daniel F. and Frank, Loren M.},
  year = {2020},
  month = jan,
  pages = {S0092867420300611},
  issn = {00928674},
  doi = {10.1016/j.cell.2020.01.014},
  abstract = {Cognitive faculties such as imagination, planning, and decision-making entail the ability to represent hypothetical experience. Crucially, animal behavior in natural settings implies that the brain can represent hypothetical future experience not only quickly but also constantly over time, as external events continually unfold. To determine how this is possible, we recorded neural activity in the hippocampus of rats navigating a maze with multiple spatial paths. We found neural activity encoding two possible future scenarios (two upcoming maze paths) in constant alternation at 8 Hz: one scenario per \$125-ms cycle. Further, we found that the underlying dynamics of cycling (both inter- and intra-cycle dynamics) generalized across qualitatively different representational correlates (location and direction). Notably, cycling occurred across moving behaviors, including during running. These findings identify a general dynamic process capable of quickly and continually representing hypothetical experience, including that of multiple possible futures.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Cell-/2020/Cell-2020-Kay et al-Constant Sub-second Cycling between Representations of Possible Futures in the.pdf},
  journal = {Cell},
  language = {en}
}

@article{Kayhippocampalnetworkspatial2016,
  title = {A Hippocampal Network for Spatial Coding during Immobility and Sleep},
  author = {Kay, Kenneth and Sosa, Marielena and Chung, Jason E. and Karlsson, Mattias P. and Larkin, Margaret C. and Frank, Loren M.},
  year = {2016},
  month = mar,
  volume = {531},
  pages = {185--190},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/nature17144},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Nature-/2016/Nature-2016-Kay et al-A hippocampal network for spatial coding during immobility and sleep.pdf},
  journal = {Nature},
  number = {7593}
}

@article{KloostermanBayesiandecodingusing2014,
  title = {Bayesian Decoding Using Unsorted Spikes in the Rat Hippocampus},
  author = {Kloosterman, Fabian and Layton, Stuart P. and Chen, Zhe and Wilson, Matthew A.},
  year = {2014},
  month = jan,
  volume = {111},
  pages = {217--227},
  issn = {0022-3077, 1522-1598},
  doi = {10.1152/jn.01046.2012},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Journal of Neurophysiology-/2014/Journal of Neurophysiology-2014-Kloosterman et al-Bayesian decoding using unsorted spikes in the rat hippocampus.pdf},
  journal = {Journal of Neurophysiology},
  language = {en},
  number = {1}
}

@inproceedings{LamNumbaLLVMbasedPython2015,
  title = {Numba: A {{LLVM}}-Based {{Python JIT}} Compiler},
  shorttitle = {Numba},
  booktitle = {Proceedings of the {{Second Workshop}} on the {{LLVM Compiler Infrastructure}} in {{HPC}} - {{LLVM}} '15},
  author = {Lam, Siu Kwan and Pitrou, Antoine and Seibert, Stanley},
  year = {2015},
  pages = {1--6},
  publisher = {{ACM Press}},
  address = {{Austin, Texas}},
  doi = {10.1145/2833157.2833162},
  abstract = {Dynamic, interpreted languages, like Python, are attractive for domain-experts and scientists experimenting with new ideas. However, the performance of the interpreter is often a barrier when scaling to larger data sets. This paper presents a just-in-time compiler for Python that focuses in scientific and array-oriented computing. Starting with the simple syntax of Python, Numba compiles a subset of the language into efficient machine code that is comparable in performance to a traditional compiled language. In addition, we share our experience in building a JIT compiler using LLVM[1].},
  file = {/Users/edeno/Zotero/storage/7FS9RSAG/Lam et al. - 2015 - Numba a LLVM-based Python JIT compiler.pdf},
  isbn = {978-1-4503-4005-2},
  language = {en}
}

@article{LeeMemorySequentialExperience2002,
  title = {Memory of {{Sequential Experience}} in the {{Hippocampus}} during {{Slow Wave Sleep}}},
  author = {Lee, Albert K. and Wilson, Matthew A.},
  year = {2002},
  month = dec,
  volume = {36},
  pages = {1183--1194},
  issn = {08966273},
  doi = {10.1016/S0896-6273(02)01096-6},
  abstract = {Rats repeatedly ran through a sequence of spatial receptive fields of hippocampal CA1 place cells in a fixed temporal order. A novel combinatorial decoding method reveals that these neurons repeatedly fired in precisely this order in long sequences involving four or more cells during slow wave sleep (SWS) immediately following, but not preceding, the experience. The SWS sequences occurred intermittently in brief (ف100 ms) bursts, each compressing the behavioral sequence in time by approximately 20-fold. This rapid encoding of sequential experience is consistent with evidence that the hippocampus is crucial for spatial learning in rodents and the formation of long-term memories of events in time in humans.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2002/Neuron-2002-Lee_Wilson-Memory of Sequential Experience in the Hippocampus during Slow Wave Sleep2.pdf},
  journal = {Neuron},
  language = {en},
  number = {6}
}

@article{LexUpSetVisualizationIntersecting2014,
  title = {{{UpSet}}: {{Visualization}} of {{Intersecting Sets}}},
  shorttitle = {{{UpSet}}},
  author = {Lex, Alexander and Gehlenborg, Nils and Strobelt, Hendrik and Vuillemot, Romain and Pfister, Hanspeter},
  year = {2014},
  month = dec,
  volume = {20},
  pages = {1983--1992},
  issn = {1077-2626},
  doi = {10.1109/TVCG.2014.2346248},
  abstract = {Understanding relationships between sets is an important analysis task that has received widespread attention in the visualization community. The major challenge in this context is the combinatorial explosion of the number of set intersections if the number of sets exceeds a trivial threshold. In this paper we introduce UpSet, a novel visualization technique for the quantitative analysis of sets, their intersections, and aggregates of intersections. UpSet is focused on creating task-driven aggregates, communicating the size and properties of aggregates and intersections, and a duality between the visualization of the elements in a dataset and their set membership. UpSet visualizes set intersections in a matrix layout and introduces aggregates based on groupings and queries. The matrix layout enables the effective representation of associated data, such as the number of elements in the aggregates and intersections, as well as additional summary statistics derived from subset or element attributes. Sorting according to various measures enables a task-driven analysis of relevant intersections and aggregates. The elements represented in the sets and their associated attributes are visualized in a separate view. Queries based on containment in specific intersections, aggregates or driven by attribute filters are propagated between both views. We also introduce several advanced visual encodings and interaction methods to overcome the problems of varying scales and to address scalability. UpSet is web-based and open source. We demonstrate its general utility in multiple use cases from various domains.},
  file = {/Users/edeno/Zotero/storage/T6PX5WQZ/Lex et al. - 2014 - UpSet Visualization of Intersecting Sets.pdf},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  language = {en},
  number = {12}
}

@article{LindermanBayesiannonparametricapproach2016,
  title = {A {{Bayesian}} Nonparametric Approach for Uncovering Rat Hippocampal Population Codes during Spatial Navigation},
  author = {Linderman, Scott W. and Johnson, Matthew J. and Wilson, Matthew A. and Chen, Zhe},
  year = {2016},
  month = apr,
  volume = {263},
  pages = {36--47},
  issn = {01650270},
  doi = {10.1016/j.jneumeth.2016.01.022},
  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 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 processhidden 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. \textcopyright{} 2016 Elsevier B.V. All rights reserved.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Journal of Neuroscience Methods-/2016/Journal of Neuroscience Methods-2016-Linderman et al-A Bayesian nonparametric approach for uncovering rat hippocampal population.pdf},
  journal = {Journal of Neuroscience Methods},
  language = {en}
}

@article{LiuMethodsAssessmentMemory2018,
  title = {Methods for {{Assessment}} of {{Memory Reactivation}}},
  author = {Liu, Shizhao and Grosmark, Andres D. and Chen, Zhe},
  year = {2018},
  month = aug,
  volume = {30},
  pages = {2175--2209},
  issn = {0899-7667, 1530-888X},
  doi = {10.1162/neco_a_01090},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neural Computation-/2018/Neural Computation-2018-Liu et al-Methods for Assessment of Memory Reactivation.pdf},
  journal = {Neural Computation},
  language = {en},
  number = {8}
}

@article{MaboudiUncoveringtemporalstructure2018,
  title = {Uncovering Temporal Structure in Hippocampal Output Patterns},
  author = {Maboudi, Kourosh and Ackermann, Etienne and {de Jong}, Laurel Watkins and Pfeiffer, Brad E and Foster, David and Diba, Kamran and Kemere, Caleb},
  year = {2018},
  pages = {24},
  doi = {10.7554/eLife.34467},
  file = {/Users/edeno/Dropbox (Personal)/Papers/undefined/undefined/Maboudi et al-Uncovering temporal structure in hippocampal output patterns.pdf},
  journal = {eLife},
  language = {en}
}

@inproceedings{McKinneyDataStructuresStatistical2010,
  title = {Data {{Structures}} for {{Statistical Computing}} in {{Python}}},
  booktitle = {Python in {{Science Conference}}},
  author = {McKinney, Wes},
  year = {2010},
  pages = {56--61},
  address = {{Austin, Texas}},
  doi = {10.25080/Majora-92bf1922-00a},
  abstract = {In this paper we are concerned with the practical issues of working with data sets common to finance, statistics, and other related fields. pandas is a new library which aims to facilitate working with these data sets and to provide a set of fundamental building blocks for implementing statistical models. We will discuss specific design issues encountered in the course of developing pandas with relevant examples and some comparisons with the R language. We conclude by discussing possible future directions for statistical computing and data analysis using Python.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/undefined/2010/2010-McKinney-Data Structures for Statistical Computing in Python.pdf},
  language = {en}
}

@article{MichonPostlearningHippocampalReplay2019,
  title = {Post-Learning {{Hippocampal Replay Selectively Reinforces Spatial Memory}} for {{Highly Rewarded Locations}}},
  author = {Michon, Fr{\'e}d{\'e}ric and Sun, Jyh-Jang and Kim, Chae Young and Ciliberti, Davide and Kloosterman, Fabian},
  year = {2019},
  month = apr,
  pages = {S096098221930346X},
  issn = {09609822},
  doi = {10.1016/j.cub.2019.03.048},
  abstract = {Offline replay of hippocampal neural patterns supports the acquisition of new tasks in novel contexts, but its contribution to consolidation of salient experiences in a familiar context is unknown. Here, we show that in a highly familiar spatial memory task, large rewards selectively enhanced performance for demanding task configurations. The reward-related enhancement was sensitive to ripple-specific disruption, and the proportion of replay events positively correlated with reward size and task demands. Hippocampal replay thus selectively enhances memory of highly rewarded locations in a familiar context.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Current Biology-/2019/Current Biology-2019-Michon et al-Post-learning Hippocampal Replay Selectively Reinforces Spatial Memory for.pdf},
  journal = {Current Biology},
  language = {en}
}

@article{NadasdyReplayTimeCompression1999,
  title = {Replay and {{Time Compression}} of {{Recurring Spike Sequences}} in the {{Hippocampus}}},
  author = {N{\'a}dasdy, Zolt{\'a}n and Hirase, Hajime and Czurk{\'o}, Andr{\'a}s and Csicsvari, Jozsef and Buzs{\'a}ki, Gy{\"o}rgy},
  year = {1999},
  month = nov,
  volume = {19},
  pages = {9497--9507},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.19-21-09497.1999},
  file = {/Users/edeno/Dropbox (Personal)/Papers/The Journal of Neuroscience-/1999/The Journal of Neuroscience-1999-Nádasdy et al-Replay and Time Compression of Recurring Spike Sequences in the Hippocampus.pdf},
  journal = {The Journal of Neuroscience},
  language = {en},
  number = {21}
}

@inproceedings{NewsonHiddenMarkovMap2009,
  title = {Hidden {{Markov Map Matching}} through {{Noise}} and {{Sparseness}}},
  booktitle = {Proceedings of the 17th {{ACM SIGSPATIAL}} International Conference on Advances in Geographic Information Systems},
  author = {Newson, Paul and Krumm, John},
  year = {2009},
  pages = {336--343},
  publisher = {{Association for Computing Machinery}},
  doi = {10.1145/1653771.1653818},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems-/2009/Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems-2009-Newson_Krumm-Hidden Markov map matching through noise and sparseness.pdf},
  series = {{{GIS}} '09}
}

@article{OlafsdottirTaskDemandsPredict2017,
  title = {Task {{Demands Predict}} a {{Dynamic Switch}} in the {{Content}} of {{Awake Hippocampal Replay}}},
  author = {{\'O}lafsd{\'o}ttir, H. Freyja and Carpenter, Francis and Barry, Caswell},
  year = {2017},
  month = nov,
  volume = {96},
  pages = {925-935.e6},
  issn = {08966273},
  doi = {10.1016/j.neuron.2017.09.035},
  abstract = {Reactivation of hippocampal place cell sequences during behavioral immobility and rest has been linked with both memory consolidation and navigational planning. Yet it remains to be investigated whether these functions are temporally segregated, occurring during different behavioral states. During a self-paced spatial task, awake hippocampal replay occurring either immediately before movement toward a reward location or just after arrival at a reward location preferentially involved cells consistent with the current trajectory. In contrast, during periods of extended immobility, no such biases were evident. Notably, the occurrence of task-focused reactivations predicted the accuracy of subsequent spatial decisions. Additionally, during immobility, but not periods preceding or succeeding movement, grid cells in deep layers of the entorhinal cortex replayed coherently with the hippocampus. Thus, hippocampal reactivations dynamically and abruptly switch between operational modes in response to task demands, plausibly moving from a state favoring navigational planning to one geared toward memory consolidation.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2017/Neuron-2017-Ólafsdóttir et al-Task Demands Predict a Dynamic Switch in the Content of Awake Hippocampal Replay.pdf},
  journal = {Neuron},
  language = {en},
  number = {4}
}

@article{OlivaOriginGammaFrequency2018,
  title = {Origin of {{Gamma Frequency Power}} during {{Hippocampal Sharp}}-{{Wave Ripples}}},
  author = {Oliva, Azahara and {Fern{\'a}ndez-Ruiz}, Antonio and {Fermino de Oliveira}, Eliezyer and Buzs{\'a}ki, Gy{\"o}rgy},
  year = {2018},
  month = nov,
  volume = {25},
  pages = {1693-1700.e4},
  issn = {22111247},
  doi = {10.1016/j.celrep.2018.10.066},
  abstract = {Hippocampal sharp-wave ripples (SPW-Rs) support consolidation of recently acquired episodic memories and planning future actions by generating ordered neuronal sequences of previous or future experiences. SPW-Rs are characterized by several spectral components: a slow (5\textendash 15 Hz) sharp-wave, a high-frequency ``ripple'' oscillation (150\textendash 200 Hz), and a slow ``gamma'' oscillation (20\textendash 40 Hz). Using laminar hippocampal recordings and optogenetic manipulations, we dissected the origin of these spectral components. We show that increased power in the 20\textendash 40 Hz band does not reflect an entrainment of CA1 and CA3 neurons at gamma frequency but the power envelope of overlapping ripples. Spike-local field potential coupling between unit firing in CA1 and CA3 regions during SPW-Rs is lowest in the gamma band. Longer SPW-Rs are preceded by increased firing in the entorhinal cortex. Thus, fusion of SPW-Rs leads to lengthening of their duration associated with increased power in the slow gamma band without the presence of true oscillation.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Cell Reports-/2018/Cell Reports-2018-Oliva et al-Origin of Gamma Frequency Power during Hippocampal Sharp-Wave Ripples.pdf;/Users/edeno/Dropbox (Personal)/Papers/Cell Reports-/2018/Cell Reports-2018-Oliva et al-Origin of Gamma Frequency Power during Hippocampal Sharp-Wave Ripples2.pdf},
  journal = {Cell Reports},
  language = {en},
  number = {7}
}

@article{OlivaRoleHippocampalCA22016,
  title = {Role of {{Hippocampal CA2 Region}} in {{Triggering Sharp}}-{{Wave Ripples}}},
  author = {Oliva, Azahara and {Fern{\'a}ndez-Ruiz}, Antonio and Buzs{\'a}ki, Gy{\"o}rgy and Ber{\'e}nyi, Antal},
  year = {2016},
  month = sep,
  volume = {91},
  pages = {1342--1355},
  issn = {08966273},
  doi = {10.1016/j.neuron.2016.08.008},
  abstract = {Sharp-wave ripples (SPW-Rs) in the hippocampus are implied in memory consolidation, as shown by observational and interventional experiments. However, the mechanism of their generation remains unclear. Using two-dimensional silicon probe arrays, we investigated the propagation of SPW-Rs across the hippocampal CA1, CA2, and CA3 subregions. Synchronous activation of CA2 ensembles preceded SPW-Rrelated population activity in CA3 and CA1 regions. Deep CA2 neurons gradually increased their activity prior to ripples and were suppressed during the population bursts of CA3-CA1 neurons (ramping cells). Activity of superficial CA2 cells preceded the activity surge in CA3-CA1 (phasic cells). The trigger role of the CA2 region in SPW-R was more pronounced during waking than sleeping. These results point to the CA2 region as an initiation zone for SPW-Rs.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2016/Neuron-2016-Oliva et al-Role of Hippocampal CA2 Region in Triggering Sharp-Wave Ripples.pdf},
  journal = {Neuron},
  language = {en},
  number = {6}
}

@article{PfeifferAutoassociativedynamicsgeneration2015,
  title = {Autoassociative Dynamics in the Generation of Sequences of Hippocampal Place Cells},
  author = {Pfeiffer, Brad E and Foster, David J},
  year = {2015},
  volume = {349},
  pages = {180--183},
  issn = {0036-8075},
  doi = {10.1126/science.aaa9633},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Science-/2015/Science-2015-Pfeiffer_Foster-Autoassociative dynamics in the generation of sequences of hippocampal place.pdf},
  journal = {Science},
  number = {6244}
}

@article{PfeifferHippocampalplacecellsequences2013,
  title = {Hippocampal Place-Cell Sequences Depict Future Paths to Remembered Goals},
  author = {Pfeiffer, Brad E. and Foster, David J.},
  year = {2013},
  month = apr,
  volume = {497},
  pages = {74--79},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/nature12112},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Nature-/2013/Nature-2013-Pfeiffer_Foster-Hippocampal place-cell sequences depict future paths to remembered goals.pdf},
  journal = {Nature},
  number = {7447}
}

@article{RanckJrStudiessingleneurons1973,
  title = {Studies on Single Neurons in Dorsal Hippocampal Formation and Septum in Unrestrained Rats: {{Part I}}. {{Behavioral}} Correlates and Firing Repertoires},
  author = {Ranck Jr, James B},
  year = {1973},
  volume = {41},
  pages = {462--531},
  publisher = {{Elsevier}},
  issn = {0014-4886},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Experimental neurology-/1973/Experimental neurology-1973-Ranck Jr-Studies on single neurons in dorsal hippocampal formation and septum in.pdf},
  journal = {Experimental neurology},
  number = {2}
}

@article{SciPy1.0ContributorsSciPyfundamentalalgorithms2020,
  title = {{{SciPy}} 1.0: Fundamental Algorithms for Scientific Computing in {{Python}}},
  shorttitle = {{{SciPy}} 1.0},
  author = {{SciPy 1.0 Contributors} and Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and {van der Walt}, St{\'e}fan J. and Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and Kern, Robert and Larson, Eric and Carey, C J and Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and VanderPlas, Jake and Laxalde, Denis and Perktold, Josef and Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and Harris, Charles R. and Archibald, Anne M. and Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and {van Mulbregt}, Paul},
  year = {2020},
  month = mar,
  volume = {17},
  pages = {261--272},
  issn = {1548-7091, 1548-7105},
  doi = {10.1038/s41592-019-0686-2},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Nature Methods-/2020/Nature Methods-2020-SciPy 1.0 Contributors et al-SciPy 1.pdf},
  journal = {Nature Methods},
  language = {en},
  number = {3}
}

@article{ShinDynamicsAwakeHippocampalPrefrontal2019,
  ids = {ShinDynamicsAwakeHippocampalPrefrontal},
  title = {Dynamics of {{Awake Hippocampal}}-{{Prefrontal Replay}} for {{Spatial Learning}} and {{Memory}}-{{Guided Decision Making}}},
  author = {Shin, Justin D. and Tang, Wenbo and Jadhav, Shantanu P.},
  year = {2019},
  month = oct,
  pages = {S0896627319307858},
  issn = {08966273},
  doi = {10.1016/j.neuron.2019.09.012},
  abstract = {Spatial learning requires remembering and choosing paths to goals. Hippocampal place cells replay spatial paths during immobility in reverse and forward order, offering a potential mechanism. However, how replay supports both goal-directed learning and memory-guided decision making is unclear. We therefore continuously tracked awake replay in the same hippocampal-prefrontal ensembles throughout learning of a spatial alternation task. We found that, during pauses between behavioral trajectories, reverse and forward hippocampal replay supports an internal cognitive search of available past and future possibilities and exhibits opposing learning gradients for prediction of past and future behavioral paths, respectively. Coordinated hippocampal-prefrontal replay distinguished correct past and future paths from alternative choices, suggesting a role in recall of past paths to guide planning of future decisions for spatial working memory. Our findings reveal a learning shift from hippocampal reverse-replay-based retrospective evaluation to forward-replay-based prospective planning, with prefrontal readout of memory-guided paths for learning and decision making.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2019/Neuron-2019-Shin et al-Dynamics of Awake Hippocampal-Prefrontal Replay for Spatial Learning and.pdf},
  journal = {Neuron},
  language = {en}
}

@article{SilvaTrajectoryeventshippocampal2015,
  title = {Trajectory Events across Hippocampal Place Cells Require Previous Experience},
  author = {Silva, Delia and Feng, Ting and Foster, David J},
  year = {2015},
  month = dec,
  volume = {18},
  pages = {1772--1779},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn.4151},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Nature Neuroscience-/2015/Nature Neuroscience-2015-Silva et al-Trajectory events across hippocampal place cells require previous experience.pdf;/Users/edeno/Dropbox (Personal)/Papers/Nature Neuroscience-/2015/Nature Neuroscience-2015-Silva et al-Trajectory events across hippocampal place cells require previous experience2.pdf},
  journal = {Nature Neuroscience},
  language = {en},
  number = {12}
}

@article{StellaHippocampalReactivationRandom2019,
  title = {Hippocampal {{Reactivation}} of {{Random Trajectories Resembling Brownian Diffusion}}},
  author = {Stella, Federico and Baracskay, Peter and O'Neill, Joseph and Csicsvari, Jozsef},
  year = {2019},
  month = feb,
  issn = {08966273},
  doi = {10.1016/j.neuron.2019.01.052},
  abstract = {Hippocampal activity patterns representing movement trajectories are reactivated in immobility and sleep periods, a process associated with memory recall, consolidation, and decision making. It is thought that only fixed, behaviorally relevant patterns can be reactivated, which are stored across hippocampal synaptic connections. To test whether some generalized rules govern reactivation, we examined trajectory reactivation following non-stereotypical exploration of familiar open-field environments. We found that random trajectories of varying lengths and timescales were reactivated, resembling that of Brownian motion of particles. The animals' behavioral trajectory did not follow Brownian diffusion demonstrating that the exact behavioral experience is not reactivated. Therefore, hippocampal circuits are able to generate random trajectories of any recently active map by following diffusion dynamics. This ability of hippocampal circuits to generate representations of all behavioral outcome combinations, experienced or not, may underlie a wide variety of hippocampal-dependent cognitive functions such as learning, generalization, and planning.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2019/Neuron-2019-Stella et al-Hippocampal Reactivation of Random Trajectories Resembling Brownian Diffusion.pdf},
  journal = {Neuron},
  language = {en}
}

@article{TangHippocampalPrefrontalReactivationLearning2017,
  title = {Hippocampal-{{Prefrontal Reactivation}} during {{Learning Is Stronger}} in {{Awake Compared}} with {{Sleep States}}},
  author = {Tang, Wenbo and Shin, Justin D. and Frank, Loren M. and Jadhav, Shantanu P.},
  year = {2017},
  month = dec,
  volume = {37},
  pages = {11789--11805},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.2291-17.2017},
  file = {/Users/edeno/Dropbox (Personal)/Papers/The Journal of Neuroscience-/2017/The Journal of Neuroscience-2017-Tang et al-Hippocampal-Prefrontal Reactivation during Learning Is Stronger in Awake.pdf},
  journal = {The Journal of Neuroscience},
  language = {en},
  number = {49}
}

@article{Tingleymethodsreactivationreplay2020,
  title = {On the Methods for Reactivation and Replay Analysis},
  author = {Tingley, David and Peyrache, Adrien},
  year = {2020},
  month = may,
  volume = {375},
  pages = {20190231},
  issn = {0962-8436, 1471-2970},
  doi = {10.1098/rstb.2019.0231},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Philosophical Transactions of the Royal Society B Biological Sciences-/2020/Philosophical Transactions of the Royal Society B Biological Sciences-2020-Tingley_Peyrache-On the methods for reactivation and replay analysis.pdf},
  journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
  language = {en},
  number = {1799}
}

@article{TingleyRoutingHippocampalRipples2020,
  title = {Routing of {{Hippocampal Ripples}} to {{Subcortical Structures}} via the {{Lateral Septum}}},
  author = {Tingley, David and Buzs{\'a}ki, Gy{\"o}rgy},
  year = {2020},
  month = jan,
  volume = {105},
  pages = {138-149.e5},
  issn = {08966273},
  doi = {10.1016/j.neuron.2019.10.012},
  abstract = {The mnemonic functions of hippocampal sharp wave ripples (SPW-Rs) have been studied extensively. Because hippocampal outputs affect not only cortical but also subcortical targets, we examined the impact of SPW-Rs on the firing patterns of lateral septal (LS) neurons in behaving rats. A large fraction of SPW-Rs were temporally locked to high-frequency oscillations (HFOs) (120\textendash 180 Hz) in LS, with strongest coupling during non-rapid eye movement (NREM) sleep, followed by waking immobility. However, coherence and spike-local field potential (LFP) coupling between the two structures were low, suggesting that HFOs are generated locally within the LS GABAergic population. This hypothesis was supported by optogenetic induction of HFOs in LS. Spiking of LS neurons was largely independent of the sequential order of spiking in SPW-Rs but instead correlated with the magnitude of excitatory synchrony of the hippocampal output. Thus, LS is strongly activated by SPW-Rs and may convey hippocampal population events to its hypothalamic and brainstem targets.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2020/Neuron-2020-Tingley_Buzsáki-Routing of Hippocampal Ripples to Subcortical Structures via the Lateral Septum.pdf},
  journal = {Neuron},
  language = {en},
  number = {1}
}

@article{TrautmannAccurateEstimationNeural2019,
  title = {Accurate {{Estimation}} of {{Neural Population Dynamics}} without {{Spike Sorting}}},
  author = {Trautmann, Eric M. and Stavisky, Sergey D. and Lahiri, Subhaneil and Ames, Katherine C. and Kaufman, Matthew T. and O'Shea, Daniel J. and Vyas, Saurabh and Sun, Xulu and Ryu, Stephen I. and Ganguli, Surya and Shenoy, Krishna V.},
  year = {2019},
  month = jul,
  volume = {103},
  pages = {292-308.e4},
  issn = {08966273},
  doi = {10.1016/j.neuron.2019.05.003},
  abstract = {A central goal of systems neuroscience is to relate an organism's neural activity to behavior. Neural population analyses often reduce the data dimensionality to focus on relevant activity patterns. A major hurdle to data analysis is spike sorting, and this problem is growing as the number of recorded neurons increases. Here, we investigate whether spike sorting is necessary to estimate neural population dynamics. The theory of random projections suggests that we can accurately estimate the geometry of lowdimensional manifolds from a small number of linear projections of the data. We recorded data using Neuropixels probes in motor cortex of nonhuman primates and reanalyzed data from three previous studies and found that neural dynamics and scientific conclusions are quite similar using multiunit threshold crossings rather than sorted neurons. This finding unlocks existing data for new analyses and informs the design and use of new electrode arrays for laboratory and clinical use.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2019/Neuron-2019-Trautmann et al-Accurate Estimation of Neural Population Dynamics without Spike Sorting.pdf},
  journal = {Neuron},
  language = {en},
  number = {2}
}

@article{TroppSneiderDifferentialbehavioralstatedependence2006,
  ids = {TroppSneiderDifferentialbehavioralstatedependence2006a},
  title = {Differential Behavioral State-Dependence in the Burst Properties of {{CA3}} and {{CA1}} Neurons},
  author = {Tropp Sneider, J. and Chrobak, J.J. and Quirk, M.C. and Oler, J.A. and Markus, E.J.},
  year = {2006},
  volume = {141},
  pages = {1665--1677},
  issn = {03064522},
  doi = {10.1016/j.neuroscience.2006.05.052},
  abstract = {Brief bursts of fast high-frequency action potentials are a signature characteristic of CA3 and CA1 pyramidal neurons. Understanding the factors determining burst and single spiking is potentially significant for sensory representation, synaptic plasticity and epileptogenesis. A variety of models suggest distinct functional roles for burst discharge, and for specific characteristics of the burst in neural coding. However, little in vivo data demonstrate how often and under what conditions CA3 and CA1 actually exhibit burst and single spike discharges.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuroscience-/2006/Neuroscience-2006-Tropp Sneider et al-Differential behavioral state-dependence in the burst properties of CA3 and CA1.pdf;/Users/edeno/Dropbox (Personal)/Papers/Neuroscience-/2006/Neuroscience-2006-Tropp Sneider et al-Differential behavioral state-dependence in the burst properties of CA3 and CA2.pdf},
  journal = {Neuroscience},
  language = {en},
  number = {4}
}

@article{vanderWaltNumPyArrayStructure2011,
  title = {The {{NumPy Array}}: {{A Structure}} for {{Efficient Numerical Computation}}},
  shorttitle = {The {{NumPy Array}}},
  author = {{van der Walt}, St{\'e}fan and Colbert, S Chris and Varoquaux, Ga{\"e}l},
  year = {2011},
  month = mar,
  volume = {13},
  pages = {22--30},
  issn = {1521-9615},
  doi = {10.1109/MCSE.2011.37},
  file = {/Users/edeno/Zotero/storage/PX4ILTTJ/van der Walt et al. - 2011 - The NumPy Array A Structure for Efficient Numeric.pdf},
  journal = {Computing in Science \& Engineering},
  language = {en},
  number = {2}
}

@article{ViterbiErrorboundsconvolutional1967,
  title = {Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm},
  author = {Viterbi, A.},
  year = {1967},
  month = apr,
  volume = {13},
  pages = {260--269},
  issn = {0018-9448, 1557-9654},
  doi = {10.1109/TIT.1967.1054010},
  file = {/Users/edeno/Dropbox (Personal)/Papers/IEEE Transactions on Information Theory-/1967/IEEE Transactions on Information Theory-1967-Viterbi-Error bounds for convolutional codes and an asymptotically optimum decoding.pdf},
  journal = {IEEE Transactions on Information Theory},
  language = {en},
  number = {2}
}

@article{WangAlternatingsequencesfuture2020a,
  ids = {WangAlternatingsequencesfuture2020},
  title = {Alternating Sequences of Future and Past Behavior Encoded within Hippocampal Theta Oscillations},
  author = {Wang, Mengni and Foster, David J. and Pfeiffer, Brad E.},
  year = {2020},
  month = oct,
  volume = {370},
  pages = {247},
  doi = {10.1126/science.abb4151},
  abstract = {Adaptive behavior requires the ability to analyze experience both prospectively and retrospectively. How can forward-ordered neural activity facilitate the storage or expression of reverse-ordered sequences? Wang et al. used multitetrode recordings from many individual hippocampal CA1 neurons in rats while simultaneously recording field potentials expressing theta oscillations. Spatial representation in the place cell network oscillated between forward and backward sweeps within each theta oscillation. Backward replay was associated with theta peaks, whereas forward replay was associated with theta troughs. Most cells fell into one category, but some corresponding to deep-layer neurons showed bimodular responses. Backward replay was driven by entorhinal inputs, whereas forward replay was evoked by CA3 inputs. These are important insights into the underlying basis of coding future and past experiences.Science, this issue p. 247Neural networks display the ability to transform forward-ordered activity patterns into reverse-ordered, retrospective sequences. The mechanisms underlying this transformation remain unknown. We discovered that, during active navigation, rat hippocampal CA1 place cell ensembles are inherently organized to produce independent forward- and reverse-ordered sequences within individual theta oscillations. This finding may provide a circuit-level basis for retrospective evaluation and storage during ongoing behavior. Theta phase procession arose in a minority of place cells, many of which displayed two preferred firing phases in theta oscillations and preferentially participated in reverse replay during subsequent rest. These findings reveal an unexpected aspect of theta-based hippocampal encoding and provide a biological mechanism to support the expression of reverse-ordered sequences.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/undefined/2020/2020-Wang et al-Alternating sequences of future and past behavior encoded within hippocampal2.pdf},
  journal = {Science},
  number = {6513}
}

@article{Waskomseabornstatisticaldata2021,
  title = {Seaborn: Statistical Data Visualization},
  shorttitle = {Seaborn},
  author = {Waskom, Michael},
  year = {2021},
  month = apr,
  volume = {6},
  pages = {3021},
  issn = {2475-9066},
  doi = {10.21105/joss.03021},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Journal of Open Source Software-/2021/Journal of Open Source Software-2021-Waskom-seaborn.pdf},
  journal = {Journal of Open Source Software},
  language = {en},
  number = {60}
}

@article{WilentDiscretePlaceFields2007,
  title = {Discrete {{Place Fields}} of {{Hippocampal Formation Interneurons}}},
  author = {Wilent, W. Bryan and Nitz, Douglas A.},
  year = {2007},
  month = jun,
  volume = {97},
  pages = {4152--4161},
  issn = {0022-3077, 1522-1598},
  doi = {10.1152/jn.01200.2006},
  abstract = {The spike discharge of hippocampal excitatory principal cells, also called ``place cells,'' is highly location specific, but the discharge of local inhibitory interneurons is thought to display relatively low spatial specificity. Whereas in other brain regions, such as sensory neocortex, the activity of interneurons is often exquisitely stimulus selective and directly determines the responses of neighboring excitatory neurons, the activity of hippocampal interneurons typically lacks the requisite specificity needed to shape the defined structure of principal cell fields. Here we show that hippocampal formation interneurons have ``on'' fields (abrupt increases in activity) and ``off'' fields (abrupt decreases in activity) that are associated with the same location-specific informational content, spatial resolution, and dependency on context as the ``place fields'' of CA1 principal cells. This establishes that interneurons have well-defined place fields, thus having important implications for understanding how the hippocampus represents spatial information.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Journal of Neurophysiology-/2007/Journal of Neurophysiology-2007-Wilent_Nitz-Discrete Place Fields of Hippocampal Formation Interneurons.pdf},
  journal = {Journal of Neurophysiology},
  language = {en},
  number = {6}
}

@article{WuHippocampalReplayCaptures2014,
  title = {Hippocampal {{Replay Captures}} the {{Unique Topological Structure}} of a {{Novel Environment}}},
  author = {Wu, X. and Foster, D. J.},
  year = {2014},
  month = may,
  volume = {34},
  pages = {6459--6469},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.3414-13.2014},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Journal of Neuroscience-/2014/Journal of Neuroscience-2014-Wu_Foster-Hippocampal Replay Captures the Unique Topological Structure of a Novel2.pdf},
  journal = {Journal of Neuroscience},
  language = {en},
  number = {19}
}

@article{YamamotoDirectMedialEntorhinal2017,
  title = {Direct {{Medial Entorhinal Cortex Input}} to {{Hippocampal CA1 Is Crucial}} for {{Extended Quiet Awake Replay}}},
  author = {Yamamoto, Jun and Tonegawa, Susumu},
  year = {2017},
  month = sep,
  volume = {96},
  pages = {217-227.e4},
  issn = {08966273},
  doi = {10.1016/j.neuron.2017.09.017},
  abstract = {Hippocampal replays have been demonstrated to play a crucial role in memory. Chains of ripples (ripple bursts) in CA1 have been reported to co-occur with long-range place cell sequence replays during the quiet awake state, but roles of neural inputs to CA1 in ripple bursts and replays are unknown. Here we show that ripple bursts in CA1 and medial entorhinal cortex (MEC) are temporally associated. An inhibition of MECIII input to CA1 during quiet awake reduced ripple bursts in CA1 and restricted the spatial coverage of replays to a shorter distance corresponding to single ripple events. The reduction did not occur with MECIII input inhibition during slowwave sleep. Inhibition of CA3 activity suppressed ripples and replays in CA1 regardless of behavioral state. Thus, MECIII input to CA1 is crucial for ripple bursts and long-range replays specifically in quiet awake, whereas CA3 input is essential for both, regardless of behavioral state.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neuron-/2017/Neuron-2017-Yamamoto_Tonegawa-Direct Medial Entorhinal Cortex Input to Hippocampal CA1 Is Crucial for.pdf},
  journal = {Neuron},
  language = {en},
  number = {1}
}

@article{YuDistincthippocampalcorticalmemory2017,
  title = {Distinct Hippocampal-Cortical Memory Representations for Experiences Associated with Movement versus Immobility},
  author = {Yu, Jai Y. and Kay, Kenneth and Liu, Daniel F. and Grossrubatscher, Irene and Loback, Adrianna and Sosa, Marielena and Chung, Jason E. and Karlsson, Mattias P. and Larkin, Margaret C. and Frank, Loren M.},
  year = {2017},
  volume = {6},
  doi = {10.7554/eLife.27621},
  file = {/Users/edeno/Dropbox (Personal)/Papers/eLife-/2017/eLife-2017-Yu et al-Distinct hippocampal-cortical memory representations for experiences associated.pdf},
  journal = {eLife}
}

@article{YuHippocampalcorticalinteraction2015,
  title = {Hippocampal\textendash Cortical Interaction in Decision Making},
  author = {Yu, Jai Y. and Frank, Loren M.},
  year = {2015},
  month = jan,
  volume = {117},
  pages = {34--41},
  issn = {10747427},
  doi = {10.1016/j.nlm.2014.02.002},
  abstract = {When making a decision it is often necessary to consider the available alternatives in order to choose the most appropriate option. This deliberative process, where the pros and cons of each option are considered, relies on memories of past actions and outcomes. The hippocampus and prefrontal cortex are required for memory encoding, memory retrieval and decision making, but it is unclear how these areas support deliberation. Here we examine the potential neural substrates of these processes in the rat. The rat is a powerful model to investigate the network mechanisms underlying deliberation in the mammalian brain given the anatomical and functional conservation of its hippocampus and prefrontal cortex to other mammalian systems. Importantly, it is amenable to large scale neural recording while performing laboratory tasks that exploit its natural decisionmaking behavior. Focusing on findings in the rat, we discuss how hippocampal-cortical interactions could provide a neural substrate for deliberative decision making.},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Neurobiology of Learning and Memory-/2015/Neurobiology of Learning and Memory-2015-Yu_Frank-Hippocampal–cortical interaction in decision making.pdf},
  journal = {Neurobiology of Learning and Memory},
  language = {en}
}

@article{ZhangInterpretingNeuronalPopulation1998,
  title = {Interpreting {{Neuronal Population Activity}} by {{Reconstruction}}: {{Unified Framework With Application}} to {{Hippocampal Place Cells}}},
  shorttitle = {Interpreting {{Neuronal Population Activity}} by {{Reconstruction}}},
  author = {Zhang, Kechen and Ginzburg, Iris and McNaughton, Bruce L. and Sejnowski, Terrence J.},
  year = {1998},
  month = feb,
  volume = {79},
  pages = {1017--1044},
  issn = {0022-3077, 1522-1598},
  doi = {10.1152/jn.1998.79.2.1017},
  file = {/Users/edeno/Dropbox (Personal)/Papers/Journal of Neurophysiology-/1998/Journal of Neurophysiology-1998-Zhang et al-Interpreting Neuronal Population Activity by Reconstruction.pdf;/Users/edeno/Dropbox (Personal)/Papers/Journal of Neurophysiology-/1998/Journal of Neurophysiology-1998-Zhang et al-Interpreting Neuronal Population Activity by Reconstruction2.pdf},
  journal = {Journal of Neurophysiology},
  language = {en},
  number = {2}
}