IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1004549.html
   My bibliography  Save this article

Establishing a Statistical Link between Network Oscillations and Neural Synchrony

Author

Listed:
  • Pengcheng Zhou
  • Shawn D Burton
  • Adam C Snyder
  • Matthew A Smith
  • Nathaniel N Urban
  • Robert E Kass

Abstract

Pairs of active neurons frequently fire action potentials or “spikes” nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons’ fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron’s firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony.Author Summary: Spike synchrony, which is widely reported in neural systems, may contribute to information transmission within and across brain regions. Critical to this theory is the potential link between oscillatory activity and synchronous spiking. In this article we provide a method for establishing the statistical association of spike synchrony with an oscillatory local field potential. We demonstrate the value of this technique by numerical simulation together with application to both in vitro and in vivo neural recordings.

Suggested Citation

  • Pengcheng Zhou & Shawn D Burton & Adam C Snyder & Matthew A Smith & Nathaniel N Urban & Robert E Kass, 2015. "Establishing a Statistical Link between Network Oscillations and Neural Synchrony," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-25, October.
    • Handle: RePEc:plo:pcbi00:1004549
    DOI: 10.1371/journal.pcbi.1004549
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004549
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004549&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1004549?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. Jonathan W. Pillow & Jonathon Shlens & Liam Paninski & Alexander Sher & Alan M. Litke & E. J. Chichilnisky & Eero P. Simoncelli, 2008. "Spatio-temporal correlations and visual signalling in a complete neuronal population," Nature, Nature, vol. 454(7207), pages 995-999, August.
    2. Laura Lee Colgin & Tobias Denninger & Marianne Fyhn & Torkel Hafting & Tora Bonnevie & Ole Jensen & May-Britt Moser & Edvard I. Moser, 2009. "Frequency of gamma oscillations routes flow of information in the hippocampus," Nature, Nature, vol. 462(7271), pages 353-357, November.
    3. Srdjan Ostojic & Nicolas Brunel, 2011. "From Spiking Neuron Models to Linear-Nonlinear Models," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-16, January.
    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. Richard Naud & Wulfram Gerstner, 2012. "Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-14, October.
    2. Skander Mensi & Olivier Hagens & Wulfram Gerstner & Christian Pozzorini, 2016. "Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-38, February.
    3. Michelle F Craft & Andrea K Barreiro & Shree Hari Gautam & Woodrow L Shew & Cheng Ly, 2021. "Differences in olfactory bulb mitral cell spiking with ortho- and retronasal stimulation revealed by data-driven models," PLOS Computational Biology, Public Library of Science, vol. 17(9), pages 1-28, September.
    4. Julian Rossbroich & Daniel Trotter & John Beninger & Katalin Tóth & Richard Naud, 2021. "Linear-nonlinear cascades capture synaptic dynamics," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-27, March.
    5. Yuxin Zhang & Yifei Yang & Xiaosi Li & Zijing Yuan & Yuki Todo & Haichuan Yang, 2023. "A Dendritic Neuron Model Optimized by Meta-Heuristics with a Power-Law-Distributed Population Interaction Network for Financial Time-Series Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-20, March.
    6. Arne F Meyer & Jan-Philipp Diepenbrock & Max F K Happel & Frank W Ohl & Jörn Anemüller, 2014. "Discriminative Learning of Receptive Fields from Responses to Non-Gaussian Stimulus Ensembles," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-15, April.
    7. Jonathan Rubin & Nachum Ulanovsky & Israel Nelken & Naftali Tishby, 2016. "The Representation of Prediction Error in Auditory Cortex," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-28, August.
    8. Omri Harish & David Hansel, 2015. "Asynchronous Rate Chaos in Spiking Neuronal Circuits," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-38, July.
    9. Franklin Leong & Babak Rahmani & Demetri Psaltis & Christophe Moser & Diego Ghezzi, 2024. "An actor-model framework for visual sensory encoding," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    10. Lucas Rudelt & Daniel González Marx & Michael Wibral & Viola Priesemann, 2021. "Embedding optimization reveals long-lasting history dependence in neural spiking activity," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-51, June.
    11. Mojtaba Chehelcheraghi & Cees van Leeuwen & Erik Steur & Chie Nakatani, 2017. "A neural mass model of cross frequency coupling," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-19, April.
    12. Fanfan Li & Dingwei Li & Chuanqing Wang & Guolei Liu & Rui Wang & Huihui Ren & Yingjie Tang & Yan Wang & Yitong Chen & Kun Liang & Qi Huang & Mohamad Sawan & Min Qiu & Hong Wang & Bowen Zhu, 2024. "An artificial visual neuron with multiplexed rate and time-to-first-spike coding," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    13. Kenneth W. Latimer & David J. Freedman, 2023. "Low-dimensional encoding of decisions in parietal cortex reflects long-term training history," Nature Communications, Nature, vol. 14(1), pages 1-24, December.
    14. 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.
    15. Braden A W Brinkman & Alison I Weber & Fred Rieke & Eric Shea-Brown, 2016. "How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-34, October.
    16. Jason S Prentice & Olivier Marre & Mark L Ioffe & Adrianna R Loback & Gašper Tkačik & Michael J Berry II, 2016. "Error-Robust Modes of the Retinal Population Code," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-32, November.
    17. Yanyun Ren & Xiaobo Bu & Ming Wang & Yue Gong & Junjie Wang & Yuyang Yang & Guijun Li & Meng Zhang & Ye Zhou & Su-Ting Han, 2022. "Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    18. Chinnakkaruppan Adaikkan & Justin Joseph & Georgios Foustoukos & Jun Wang & Denis Polygalov & Roman Boehringer & Steven J. Middleton & Arthur J. Y. Huang & Li-Huei Tsai & Thomas J. McHugh, 2024. "Silencing CA1 pyramidal cells output reveals the role of feedback inhibition in hippocampal oscillations," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    19. Jan Humplik & Gašper Tkačik, 2017. "Probabilistic models for neural populations that naturally capture global coupling and criticality," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-26, September.
    20. Anirban Das & Alec G. Sheffield & Anirvan S. Nandy & Monika P. Jadi, 2024. "Brain-state mediated modulation of inter-laminar dependencies in visual cortex," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

    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:pcbi00:1004549. 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.