Elsevier

Current Opinion in Neurobiology

Volume 64, October 2020, Pages 111-118
Current Opinion in Neurobiology

Cell assemblies, sequences and temporal coding in the hippocampus

https://doi.org/10.1016/j.conb.2020.03.003Get rights and content

Highlights

  • • A form of temporal coding is the plasticity in replay of a recent experience.

  • • Robust theta sequences are required for the occurrence of plasticity in replay.

  • • Plasticity in replay is primarily contributed by temporal coordination coding.

  • • Increases in tuning to cell assembly and tuplet repeats support temporal coding.

Like social networks, neurons in the brain are organized in neuronal ensembles that constrain and at the same time enrich the role and temporal precision of activity of individual neurons. Changes in coordinated firing across cortical neurons as well as selective changes in timing and sequential order across neurons that are important for encoding of novel information have collectively been known as ensemble temporal coding. Here we review recent findings on the role of online and offline temporal coding within sequential cell assemblies from the rodent hippocampus thought be important for memory encoding and consolidation and for spatial navigation. We propose that temporal coding in the rodent hippocampus represented as plasticity in replay activity relies primarily on subtle and selective changes in coordinated firing within the microstructure of individual cell assembly organization during sleep.

Introduction

Information processing in the brain relies on interconnected networks of neurons. Single neurons perform information encoding by both rate changes within and across receptive fields and temporal coding by phase preference and phase precession within their receptive fields [1,2]. Changes in coordinated firing across individual neurons as well as selective changes in timing and sequential order across neurons induced by novel experiences have collectively been known as ensemble temporal coding [3,4]. However, first, genuine neuronal ensemble temporal coding can be hard to distinguish from independent or correlated changes in firing rates of populations of neurons, which can often give the appearance of increased coordination and sequential organization of the neurons [5,6]. Second, temporal coding would need to be distinguished from the default neuronal organization in Hebbian cell assemblies and into temporal sequences of firing also known as Hebbian phase sequence that both operate at (tens of) millisecond timescales [7,8]. Third, as the default neuronal activity is temporally organized into coordinated firing within cell assemblies and sequential firing within and predominantly across cell assemblies even during slow-wave sleep, when the brain is fairly disconnected from the external world, the coding aspect of temporal coding will need to be further refined.

Here, we propose that specific changes in temporal organization of spontaneous neuronal ensemble activity from the sleep preceding to the one following a novel experience can be used to infer and validate the changes associated with coding during the experience and be considered a form of network learning and representation by temporal coding. Using an overview of data published in the recent years, we further propose that this temporal coding relies primarily on subtle and selective changes in coordinated firing within individual cell assemblies already part of a temporal sequence, in line with a predictive internal model. Finally, we describe the changes in the microstructure of neuronal firing within cell assemblies as the basis for the ensemble temporal coding using data on spatial coding in the rodent hippocampus.

Section snippets

State-dependent temporal sequences of firing: theta sequences, replay, and preplay

The rodent hippocampus has proved to be an excellent and productive model system in which to study the basic principles of internally generated representations in the brain, in the form of episodic memories [9] and ‘cognitive maps’ [1]. In the adult rodent, in addition to individual place cells that represent specific spatial locations along an animal trajectory at ‘clock time’ scale during theta oscillations, ensembles of pyramidal cells in the hippocampus are organized into temporo-spatial

Theta sequences and the plasticity in temporal sequences during sleep

Given the complex multisensory, associative nature of hippocampal activity and the existence of default temporally compressed sequences of firing during sleep, a convincing assessment of pure temporal coding of presumed sequential stimuli encountered during rodent navigation has remained difficult. The mnemonic features repeatedly demonstrated for the hippocampal formation have provided us with an opportunity to infer temporal encoding during navigation by decoding and evaluating the plastic

The character of replay plasticity: subtle, selective, cell-assembly coordination-based

The plasticity in decoded representations of previous navigational experience is generally detected by performing Bayesian decoding of neuronal activity during sleep. Several coding schemes combine to contribute to the decoded representations during sleep: rate coding, temporal sequence coding, and temporal coordination coding. Rate coding is primarily represented by changes in neuronal firing rates during the sleep after a navigational experience as a function of their firing rates during the

Microstructure of cell assembly activation and plasticity underlying temporal coding

Recent work has shown that interfering with cell assembly activation during offline epochs of neuronal activity during post encoding rest and sleep can affect learning and behavior performance of the animals [57,63••,64]. This suggests that offline temporal coordination coding, which relies on cell assembly activation, could play a critical role in stabilization and consolidation of a recent memory. Conversely, increased neuronal participation and cell assembly activation by artificially

Conclusions

The relationship between cell assembly organization and temporal coding has traditionally been difficult to draw. This was in part because the fine temporal organization of neuronal activity in cell assemblies and temporal sequences in the hippocampus appears to follow temporal coding principles even during offline states like slow wave sleep when the brain is fairly disconnected from the external world [23,46,56]. Part of this functional organization represents default sequential activity of

Author contributions

G.D. conceived and wrote the manuscript.

Conflict of interest statement

Nothing declared.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

We thank all the members of the Dragoi lab for their contribution to the primary experimental research work that led to some of the findings included in this opinion. This work was supported by funding from the NINDS of the NIH under award number R01NS104917 and from NIMH of the NIH under award number R01MH121372 to G.D. The funding sources had no involvement in the content of this manuscript.

References (69)

  • B. Giri et al.

    Hippocampal reactivation extends for several hours following novel experience

    J Neurosci

    (2019)
  • J. O’Keefe et al.

    The Hippocampus as a Cognitive Map

    (1978)
  • J. O’Keefe et al.

    Phase relationship between hippocampal place units and the EEG theta rhythm

    Hippocampus

    (1993)
  • K.D. Harris et al.

    Organization of cell assemblies in the hippocampus

    Nature

    (2003)
  • A. Peyrache et al.

    Sequential reinstatement of neocortical activity during slow oscillations depends on cells’ global activity

    Front Syst Neurosci

    (2010)
  • D.O. Hebb

    The Organization of Behavior: A Neuropsychological Theory

    (1949)
  • K.D. Harris

    Neural signatures of cell assembly organization

    Nat Rev Neurosci

    (2005)
  • E. Tulving

    Episodic and semantic memory

  • D.J. Foster et al.

    Hippocampal theta sequences

    Hippocampus

    (2007)
  • W.E. Skaggs et al.

    Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences

    Hippocampus

    (1996)
  • A.S. Gupta et al.

    Segmentation of spatial experience by hippocampal theta sequences

    Nat Neurosci

    (2012)
  • O. Jensen et al.

    Position reconstruction from an ensemble of hippocampal place cells: contribution of theta phase coding

    J Neurophysiol

    (2000)
  • K.D. Harris et al.

    Spike train dynamics predicts theta-related phase precession in hippocampal pyramidal cells

    Nature

    (2002)
  • S.J. Middleton et al.

    Silencing CA3 disrupts temporal coding in the CA1 ensemble

    Nat Neurosci

    (2016)
  • M.I. Schlesiger et al.

    The medial entorhinal cortex is necessary for temporal organization of hippocampal neuronal activity

    Nat Neurosci

    (2015)
  • G. Dragoi et al.

    Development of schemas revealed by prior experience and NMDA receptor knock-out

    eLife

    (2013)
  • Y. Wang et al.

    Theta sequences are essential for internally generated hippocampal firing fields

    Nat Neurosci

    (2015)
  • E. Pastalkova et al.

    Internally generated cell assembly sequences in the rat hippocampus

    Science

    (2008)
  • C. Pavlides et al.

    Influences of hippocampal place cell firing in the awake state on the activity of these cells during subsequent sleep episodes

    J Neurosci

    (1989)
  • M.A. Wilson et al.

    Reactivation of hippocampal ensemble memories during sleep

    Science

    (1994)
  • Z. Nadasdy et al.

    Replay and time compression of recurring spike sequences in the hippocampus

    J Neurosci

    (1999)
  • D. Ji et al.

    Coordinated memory replay in the visual cortex and hippocampus during sleep

    Nat Neurosci

    (2007)
  • D.J. Foster et al.

    Reverse replay of behavioural sequences in hippocampal place cells during the awake state

    Nature

    (2006)
  • M.P. Karlsson et al.

    Awake replay of remote experiences in the hippocampus

    Nat Neurosci

    (2009)
  • Cited by (23)

    • The hippocampal formation as a hierarchical generative model supporting generative replay and continual learning

      2022, Progress in Neurobiology
      Citation Excerpt :

      Specifically, the study reported rank-order correlations between pre-run hippocampal activity (i.e., replays measured before navigational experience) and post-run activity (i.e., replays measured after navigational experience) to be significantly greater than rank-order correlations between run activity (i.e., hippocampal activity measured during navigational experience) and post-run activity. This finding suggests that hippocampal temporal coding depends primarily on selective changes to preconfigured network dynamics (Dragoi, 2020). To test whether our model reproduces the above empirical findings, we performed the same rank-order analysis as in (Liu et al., 2019) on our generative replay and prioritized generative replay agents.

    • Orientation selectivity enhances context generalization and generative predictive coding in the hippocampus

      2021, Neuron
      Citation Excerpt :

      Here we reveal that incorporation of generalized multisensory information about orientation and topology into the predictive codes of sleep strengthens the combined network predictive power for similar (parallel) future novel environments (Figures 6G and 6H). The degree by which the predictive codes were strengthened by parallel track generalization is commensurate with that of the plasticity in replay (Dragoi, 2020) induced by the actual exploration of the new (parallel) environments (Figures 6G and 6H). This suggests that mapping of an even larger repertoire of multisensory-driven hippocampal activity could further increase the network predictability for rapid encoding and representation of future novel experiences.

    • Editorial overview: Systems neuroscience

      2020, Current Opinion in Neurobiology
    View all citing articles on Scopus
    View full text