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Organization of cell assemblies in the hippocampus

Author

Listed:
  • Kenneth D. Harris

    (The State University of New Jersey)

  • Jozsef Csicsvari

    (The State University of New Jersey
    MRC Anatomical Neuropharmacology Unit)

  • Hajime Hirase

    (The State University of New Jersey)

  • George Dragoi

    (The State University of New Jersey)

  • György Buzsáki

    (The State University of New Jersey)

Abstract

Neurons can produce action potentials with high temporal precision1. A fundamental issue is whether, and how, this capability is used in information processing. According to the ‘cell assembly’ hypothesis, transient synchrony of anatomically distributed groups of neurons underlies processing of both external sensory input and internal cognitive mechanisms2,3,4. Accordingly, neuron populations should be arranged into groups whose synchrony exceeds that predicted by common modulation by sensory input. Here we find that the spike times of hippocampal pyramidal cells can be predicted more accurately by using the spike times of simultaneously recorded neurons in addition to the animals location in space. This improvement remained when the spatial prediction was refined with a spatially dependent theta phase modulation5,6,7,8. The time window in which spike times are best predicted from simultaneous peer activity is 10–30 ms, suggesting that cell assemblies are synchronized at this timescale. Because this temporal window matches the membrane time constant of pyramidal neurons9, the period of the hippocampal gamma oscillation10 and the time window for synaptic plasticity11, we propose that cooperative activity at this timescale is optimal for information transmission and storage in cortical circuits.

Suggested Citation

  • Kenneth D. Harris & Jozsef Csicsvari & Hajime Hirase & George Dragoi & György Buzsáki, 2003. "Organization of cell assemblies in the hippocampus," Nature, Nature, vol. 424(6948), pages 552-556, July.
    • Handle: RePEc:nat:nature:v:424:y:2003:i:6948:d:10.1038_nature01834
    DOI: 10.1038/nature01834
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    Cited by:

    1. Gray Umbach & Ryan Tan & Joshua Jacobs & Brad E. Pfeiffer & Bradley Lega, 2022. "Flexibility of functional neuronal assemblies supports human memory," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Omer Hazon & Victor H. Minces & David P. Tomàs & Surya Ganguli & Mark J. Schnitzer & Pablo E. Jercog, 2022. "Noise correlations in neural ensemble activity limit the accuracy of hippocampal spatial representations," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Remus Oşan & Liping Zhu & Shy Shoham & Joe Z Tsien, 2007. "Subspace Projection Approaches to Classification and Visualization of Neural Network-Level Encoding Patterns," PLOS ONE, Public Library of Science, vol. 2(5), pages 1-14, May.
    4. Asako Noguchi & Roman Huszár & Shota Morikawa & György Buzsáki & Yuji Ikegaya, 2022. "Inhibition allocates spikes during hippocampal ripples," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    5. Seif Eldawlatly & Karim G Oweiss, 2011. "Millisecond-Timescale Local Network Coding in the Rat Primary Somatosensory Cortex," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-14, June.
    6. Robert R Kerr & Anthony N Burkitt & Doreen A Thomas & Matthieu Gilson & David B Grayden, 2013. "Delay Selection by Spike-Timing-Dependent Plasticity in Recurrent Networks of Spiking Neurons Receiving Oscillatory Inputs," PLOS Computational Biology, Public Library of Science, vol. 9(2), pages 1-19, February.
    7. Oliver Barnstedt & Petra Mocellin & Stefan Remy, 2024. "A hippocampus-accumbens code guides goal-directed appetitive behavior," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    8. Eleonora Russo & Nadine Becker & Aleks P. F. Domanski & Timothy Howe & Kipp Freud & Daniel Durstewitz & Matthew W. Jones, 2024. "Integration of rate and phase codes by hippocampal cell-assemblies supports flexible encoding of spatiotemporal context," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    9. Giovanni Diana & Thomas T J Sainsbury & Martin P Meyer, 2019. "Bayesian inference of neuronal assemblies," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-31, October.
    10. Mamiko Arai & Vicky Brandt & Yuri Dabaghian, 2014. "The Effects of Theta Precession on Spatial Learning and Simplicial Complex Dynamics in a Topological Model of the Hippocampal Spatial Map," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-14, June.
    11. Guillaume Viejo & Thomas Cortier & Adrien Peyrache, 2018. "Brain-state invariant thalamo-cortical coordination revealed by non-linear encoders," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-25, March.
    12. Stefan Habenschuss & Zeno Jonke & Wolfgang Maass, 2013. "Stochastic Computations in Cortical Microcircuit Models," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-28, November.
    13. Dhanya Parameshwaran & Upinder S Bhalla, 2013. "Theta Frequency Background Tunes Transmission but Not Summation of Spiking Responses," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-12, January.
    14. Shohei Hidaka & Masafumi Oizumi, 2018. "Fast and exact search for the partition with minimal information loss," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-14, September.
    15. Maxim Volgushev & Vladimir Ilin & Ian H Stevenson, 2015. "Identifying and Tracking Simulated Synaptic Inputs from Neuronal Firing: Insights from In Vitro Experiments," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-31, March.
    16. Caleb Kemere & Margaret F Carr & Mattias P Karlsson & Loren M Frank, 2013. "Rapid and Continuous Modulation of Hippocampal Network State during Exploration of New Places," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
    17. Carina Curto & Vladimir Itskov, 2008. "Cell Groups Reveal Structure of Stimulus Space," PLOS Computational Biology, Public Library of Science, vol. 4(10), pages 1-13, October.
    18. Einat Granot-Atedgi & Gašper Tkačik & Ronen Segev & Elad Schneidman, 2013. "Stimulus-dependent Maximum Entropy Models of Neural Population Codes," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-14, March.

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