IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1000551.html
   My bibliography  Save this article

Specific Entrainment of Mitral Cells during Gamma Oscillation in the Rat Olfactory Bulb

Author

Listed:
  • François O David
  • Etienne Hugues
  • Tristan Cenier
  • Nicolas Fourcaud-Trocmé
  • Nathalie Buonviso

Abstract

Local field potential (LFP) oscillations are often accompanied by synchronization of activity within a widespread cerebral area. Thus, the LFP and neuronal coherence appear to be the result of a common mechanism that underlies neuronal assembly formation. We used the olfactory bulb as a model to investigate: (1) the extent to which unitary dynamics and LFP oscillations can be correlated and (2) the precision with which a model of the hypothesized underlying mechanisms can accurately explain the experimental data. For this purpose, we analyzed simultaneous recordings of mitral cell (MC) activity and LFPs in anesthetized and freely breathing rats in response to odorant stimulation. Spike trains were found to be phase-locked to the gamma oscillation at specific firing rates and to form odor-specific temporal patterns. The use of a conductance-based MC model driven by an approximately balanced excitatory-inhibitory input conductance and a relatively small inhibitory conductance that oscillated at the gamma frequency allowed us to provide one explanation of the experimental data via a mode-locking mechanism. This work sheds light on the way network and intrinsic MC properties participate in the locking of MCs to the gamma oscillation in a realistic physiological context and may result in a particular time-locked assembly. Finally, we discuss how a self-synchronization process with such entrainment properties can explain, under experimental conditions: (1) why the gamma bursts emerge transiently with a maximal amplitude position relative to the stimulus time course; (2) why the oscillations are prominent at a specific gamma frequency; and (3) why the oscillation amplitude depends on specific stimulus properties. We also discuss information processing and functional consequences derived from this mechanism.Author Summary: Olfactory function relies on a chain of neural relays that extends from the periphery to the central nervous system and implies neural activity with various timescales. A central question in neuroscience is how information is encoded by the neural activity. In the mammalian olfactory bulb, local neural activity oscillations in the 40–80 Hz range (gamma) may influence the timing of individual neuron activities such that olfactory information may be encoded in this way. In this study, we first characterize in vivo the detailed activity of individual neurons relative to the oscillation and find that, depending on their state, neurons can exhibit periodic activity patterns. We also find, at least qualitatively, a relation between this activity and a particular odor. This is reminiscent of general physical phenomena—the entrainment by an oscillation—and to verify this hypothesis, in a second phase, we build a biologically realistic model mimicking these in vivo conditions. Our model confirms quantitatively this hypothesis and reveals that entrainment is maximal in the gamma range. Taken together, our results suggest that the neuronal activity may be specifically formatted in time during the gamma oscillation in such a way that it could, at this stage, encode the odor.

Suggested Citation

  • François O David & Etienne Hugues & Tristan Cenier & Nicolas Fourcaud-Trocmé & Nathalie Buonviso, 2009. "Specific Entrainment of Mitral Cells during Gamma Oscillation in the Rat Olfactory Bulb," PLOS Computational Biology, Public Library of Science, vol. 5(10), pages 1-19, October.
  • Handle: RePEc:plo:pcbi00:1000551
    DOI: 10.1371/journal.pcbi.1000551
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000551
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000551&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1000551?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Stijn Cassenaer & Gilles Laurent, 2007. "Hebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts," Nature, Nature, vol. 448(7154), pages 709-713, August.
    2. Tomáš Hromádka & Michael R DeWeese & Anthony M Zador, 2008. "Sparse Representation of Sounds in the Unanesthetized Auditory Cortex," PLOS Biology, Public Library of Science, vol. 6(1), pages 1-14, January.
    3. Eugenio Rodriguez & Nathalie George & Jean-Philippe Lachaux & Jacques Martinerie & Bernard Renault & Francisco J. Varela, 1999. "Perception's shadow: long-distance synchronization of human brain activity," Nature, Nature, vol. 397(6718), pages 430-433, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Oren Amsalem & Hidehiko Inagaki & Jianing Yu & Karel Svoboda & Ran Darshan, 2024. "Sub-threshold neuronal activity and the dynamical regime of cerebral cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. Jajcay, Nikola, 2018. "Spatial and temporal scales of atmospheric dynamics," Thesis Commons ar8ks_v1, Center for Open Science.
    3. Corentin Massot & Adam D Schneider & Maurice J Chacron & Kathleen E Cullen, 2012. "The Vestibular System Implements a Linear–Nonlinear Transformation In Order to Encode Self-Motion," PLOS Biology, Public Library of Science, vol. 10(7), pages 1-20, July.
    4. Michael A Carlin & Mounya Elhilali, 2013. "Sustained Firing of Model Central Auditory Neurons Yields a Discriminative Spectro-temporal Representation for Natural Sounds," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-18, March.
    5. Christian G Fink & Victoria Booth & Michal Zochowski, 2011. "Cellularly-Driven Differences in Network Synchronization Propensity Are Differentially Modulated by Firing Frequency," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-14, May.
    6. Andreas Wilmer & Marc de Lussanet & Markus Lappe, 2012. "Time-Delayed Mutual Information of the Phase as a Measure of Functional Connectivity," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-22, September.
    7. Michael N Economo & John A White, 2012. "Membrane Properties and the Balance between Excitation and Inhibition Control Gamma-Frequency Oscillations Arising from Feedback Inhibition," PLOS Computational Biology, Public Library of Science, vol. 8(1), pages 1-20, January.
    8. Mathieu N Galtier & Jonathan Touboul, 2013. "Macroscopic Equations Governing Noisy Spiking Neuronal Populations with Linear Synapses," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-11, November.
    9. Hu, Xueyan & Ding, Qianming & Wu, Yong & Huang, Weifang & Yang, Lijian & Jia, Ya, 2024. "Dynamical rewiring promotes synchronization in memristive FitzHugh-Nagumo neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    10. Adeeti Aggarwal & Connor Brennan & Jennifer Luo & Helen Chung & Diego Contreras & Max B. Kelz & Alex Proekt, 2022. "Visual evoked feedforward–feedback traveling waves organize neural activity across the cortical hierarchy in mice," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    11. Yifan Gu & Yang Qi & Pulin Gong, 2019. "Rich-club connectivity, diverse population coupling, and dynamical activity patterns emerging from local cortical circuits," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-34, April.
    12. Wu, Huagan & Bian, Yixuan & Zhang, Yunzhen & Guo, Yixuan & Xu, Quan & Chen, Mo, 2023. "Multi-stable states and synchronicity of a cellular neural network with memristive activation function," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    13. Wu, Yong & Ding, Qianming & Huang, Weifang & Hu, Xueyan & Ye, Zhiqiu & Jia, Ya, 2024. "Dynamic modulation of external excitation enhance synchronization in complex neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    14. Perla González-Pereyra & Oswaldo Sánchez-Lobato & Mario G. Martínez-Montalvo & Diana I. Ortega-Romero & Claudia I. Pérez-Díaz & Hugo Merchant & Luis A. Tellez & Pavel E. Rueda-Orozco, 2024. "Preconfigured cortico-thalamic neural dynamics constrain movement-associated thalamic activity," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    15. Sanjoy Dasgupta & Daisuke Hattori & Saket Navlakha, 2022. "A neural theory for counting memories," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    16. Cheng Ly & Brent Doiron, 2009. "Divisive Gain Modulation with Dynamic Stimuli in Integrate-and-Fire Neurons," PLOS Computational Biology, Public Library of Science, vol. 5(4), pages 1-12, April.
    17. Gonzalo H Otazu & Christian Leibold, 2011. "A Corticothalamic Circuit Model for Sound Identification in Complex Scenes," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-15, September.
    18. Jean-Pierre Rospars & Alexandre Grémiaux & David Jarriault & Antoine Chaffiol & Christelle Monsempes & Nina Deisig & Sylvia Anton & Philippe Lucas & Dominique Martinez, 2014. "Heterogeneity and Convergence of Olfactory First-Order Neurons Account for the High Speed and Sensitivity of Second-Order Neurons," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-16, December.
    19. Muto, Makoto & Saiki, Yoshitaka, 2024. "Synchronization analysis between exchange rates on the basis of purchasing power parity using the Hilbert transform," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    20. Cao, Haoyu & Yang, Zhiyin & Liu, Zonghua, 2023. "Remote synchronization in multi-layered community networks with star-like topology," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1000551. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.