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Correlation between neural spike trains increases with firing rate

Author

Listed:
  • Jaime de la Rocha

    (Center for Neural Science, New York University)

  • Brent Doiron

    (Center for Neural Science, New York University
    Courant Institute of Mathematical Sciences, New York University
    Present address: Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA.)

  • Eric Shea-Brown

    (Center for Neural Science, New York University
    Courant Institute of Mathematical Sciences, New York University)

  • Krešimir Josić

    (University of Houston, Houston, Texas 77204, USA)

  • Alex Reyes

    (Center for Neural Science, New York University)

Abstract

Deciphering a 'neural code' usually requires measurement of either the rate of spike (electrical impulses) production or the spike synchrony. However, these two measures are not independent, as higher rates are associated with higher synchrony. It is further shown that the connection between rate and synchrony enhances information coding.

Suggested Citation

  • Jaime de la Rocha & Brent Doiron & Eric Shea-Brown & Krešimir Josić & Alex Reyes, 2007. "Correlation between neural spike trains increases with firing rate," Nature, Nature, vol. 448(7155), pages 802-806, August.
    • Handle: RePEc:nat:nature:v:448:y:2007:i:7155:d:10.1038_nature06028
    DOI: 10.1038/nature06028
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    Citations

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    Cited by:

    1. Ashok Litwin-Kumar & Anne-Marie M Oswald & Nathaniel N Urban & Brent Doiron, 2011. "Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-14, December.
    2. Gabriel Koch Ocker & Krešimir Josić & Eric Shea-Brown & Michael A Buice, 2017. "Linking structure and activity in nonlinear spiking networks," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-47, June.
    3. Stefano Recanatesi & Gabriel Koch Ocker & Michael A Buice & Eric Shea-Brown, 2019. "Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-29, July.
    4. Geoffrey Terral & Evan Harrell & Gabriel Lepousez & Yohan Wards & Dinghuang Huang & Tiphaine Dolique & Giulio Casali & Antoine Nissant & Pierre-Marie Lledo & Guillaume Ferreira & Giovanni Marsicano & , 2024. "Endogenous cannabinoids in the piriform cortex tune olfactory perception," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    5. Moritz Helias & Moritz Deger & Stefan Rotter & Markus Diesmann, 2010. "Instantaneous Non-Linear Processing by Pulse-Coupled Threshold Units," PLOS Computational Biology, Public Library of Science, vol. 6(9), pages 1-10, September.
    6. Volker Pernice & Rava Azeredo da Silveira, 2018. "Interpretation of correlated neural variability from models of feed-forward and recurrent circuits," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-26, February.
    7. Andrea K Barreiro & Cheng Ly, 2017. "When do correlations increase with firing rates in recurrent networks?," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-30, April.
    8. Robert Loshkarev & Dmitry Postnov, 2021. "Toward Minimalistic Model of Cellular Volume Dynamics in Neurovascular Unit," Mathematics, MDPI, vol. 9(19), pages 1-13, September.
    9. Francesco Negro & Dario Farina, 2012. "Factors Influencing the Estimates of Correlation between Motor Unit Activities in Humans," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-14, September.
    10. Emiliano Torre & Carlos Canova & Michael Denker & George Gerstein & Moritz Helias & Sonja Grün, 2016. "ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-34, July.
    11. Yu Hu & Joel Zylberberg & Eric Shea-Brown, 2014. "The Sign Rule and Beyond: Boundary Effects, Flexibility, and Noise Correlations in Neural Population Codes," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-22, February.
    12. Gabriel Koch Ocker & Ashok Litwin-Kumar & Brent Doiron, 2015. "Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-40, August.
    13. Christian Donner & Klaus Obermayer & Hideaki Shimazaki, 2017. "Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-27, January.
    14. 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.
    15. Volker Pernice & Benjamin Staude & Stefano Cardanobile & Stefan Rotter, 2011. "How Structure Determines Correlations in Neuronal Networks," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-14, May.
    16. Tom Tetzlaff & Moritz Helias & Gaute T Einevoll & Markus Diesmann, 2012. "Decorrelation of Neural-Network Activity by Inhibitory Feedback," PLOS Computational Biology, Public Library of Science, vol. 8(8), pages 1-29, August.
    17. Gaëlle Desbordes & Jianzhong Jin & Chong Weng & Nicholas A Lesica & Garrett B Stanley & Jose-Manuel Alonso, 2008. "Timing Precision in Population Coding of Natural Scenes in the Early Visual System," PLOS Biology, Public Library of Science, vol. 6(12), pages 1-11, December.
    18. 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.
    19. James Trousdale & Yu Hu & Eric Shea-Brown & Krešimir Josić, 2012. "Impact of Network Structure and Cellular Response on Spike Time Correlations," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-15, March.
    20. Moritz Helias & Tom Tetzlaff & Markus Diesmann, 2014. "The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-21, January.
    21. Stojan Jovanović & Stefan Rotter, 2016. "Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-28, June.

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