IDEAS home Printed from https://ideas.repec.org/a/plo/pbio00/0060324.html
   My bibliography  Save this article

Timing Precision in Population Coding of Natural Scenes in the Early Visual System

Author

Listed:
  • Gaëlle Desbordes
  • Jianzhong Jin
  • Chong Weng
  • Nicholas A Lesica
  • Garrett B Stanley
  • Jose-Manuel Alonso

Abstract

The timing of spiking activity across neurons is a fundamental aspect of the neural population code. Individual neurons in the retina, thalamus, and cortex can have very precise and repeatable responses but exhibit degraded temporal precision in response to suboptimal stimuli. To investigate the functional implications for neural populations in natural conditions, we recorded in vivo the simultaneous responses, to movies of natural scenes, of multiple thalamic neurons likely converging to a common neuronal target in primary visual cortex. We show that the response of individual neurons is less precise at lower contrast, but that spike timing precision across neurons is relatively insensitive to global changes in visual contrast. Overall, spike timing precision within and across cells is on the order of 10 ms. Since closely timed spikes are more efficient in inducing a spike in downstream cortical neurons, and since fine temporal precision is necessary to represent the more slowly varying natural environment, we argue that preserving relative spike timing at a ∼10-ms resolution is a crucial property of the neural code entering cortex. : Neurons convey information about the world in the form of trains of action potentials (spikes). These trains are highly repeatable when the same stimulus is presented multiple times, and this temporal precision across repetitions can be as fine as a few milliseconds. It is usually assumed that this time scale also corresponds to the timing precision of several neighboring neurons firing in concert. However, the relative timing of spikes emitted by different neurons in a local population is not necessarily as fine as the temporal precision across repetitions within a single neuron. In the visual system of the brain, the level of contrast in the image entering the retina can affect single-neuron temporal precision, but the effects of contrast on the neural population code are unknown. Here we show that the temporal scale of the population code entering visual cortex is on the order of 10 ms and is largely insensitive to changes in visual contrast. Since closely timed spikes are more efficient in inducing a spike in downstream cortical neurons, and since fine temporal precision is necessary in representing the more slowly varying natural environment, preserving relative spike timing at a ∼10-ms resolution may be a crucial property of the neural code entering cortex. Early neural representation of visual scenes occurs with a temporal precision on the order of 10 ms, which is precise enough to strongly drive downstream neurons in the visual pathway. Unlike individual neurons, the neural population code is largely insensitive to pronounced changes in visual contrast.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pbio00:0060324
    DOI: 10.1371/journal.pbio.0060324
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0060324
    Download Restriction: no

    File URL: https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.0060324&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pbio.0060324?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. Elad Schneidman & Michael J. Berry & Ronen Segev & William Bialek, 2006. "Weak pairwise correlations imply strongly correlated network states in a neural population," Nature, Nature, vol. 440(7087), pages 1007-1012, April.
    2. Daniel A. Butts & Chong Weng & Jianzhong Jin & Chun-I Yeh & Nicholas A. Lesica & Jose-Manuel Alonso & Garrett B. Stanley, 2007. "Temporal precision in the neural code and the timescales of natural vision," Nature, Nature, vol. 449(7158), pages 92-95, September.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ovidiu F Jurjuţ & Danko Nikolić & Wolf Singer & Shan Yu & Martha N Havenith & Raul C Mureşan, 2011. "Timescales of Multineuronal Activity Patterns Reflect Temporal Structure of Visual Stimuli," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-15, February.
    2. Sean T Kelly & Jens Kremkow & Jianzhong Jin & Yushi Wang & Qi Wang & Jose-Manuel Alonso & Garrett B Stanley, 2014. "The Role of Thalamic Population Synchrony in the Emergence of Cortical Feature Selectivity," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-13, January.

    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. Ovidiu F Jurjuţ & Danko Nikolić & Wolf Singer & Shan Yu & Martha N Havenith & Raul C Mureşan, 2011. "Timescales of Multineuronal Activity Patterns Reflect Temporal Structure of Visual Stimuli," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-15, February.
    2. Rava Azeredo da Silveira & Michael J Berry II, 2014. "High-Fidelity Coding with Correlated Neurons," PLOS Computational Biology, Public Library of Science, vol. 10(11), pages 1-25, November.
    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. 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.
    5. 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.
    6. 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.
    7. Lipovetsky, Stan, 2018. "Quantum paradigm of probability amplitude and complex utility in entangled discrete choice modeling," Journal of choice modelling, Elsevier, vol. 27(C), pages 62-73.
    8. Mark L Ioffe & Michael J Berry II, 2017. "The structured ‘low temperature’ phase of the retinal population code," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-31, October.
    9. Katarína Bod’ová & Enikő Szép & Nicholas H Barton, 2021. "Dynamic maximum entropy provides accurate approximation of structured population dynamics," PLOS Computational Biology, Public Library of Science, vol. 17(12), pages 1-22, December.
    10. MohammadReza Zahedian & Mahsa Bagherikalhor & Andrey Trufanov & G. Reza Jafari, 2022. "Financial Crisis in the Framework of Non-zero Temperature Balance Theory," Papers 2202.03198, arXiv.org.
    11. Yasser Roudi & Sheila Nirenberg & Peter E Latham, 2009. "Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't," PLOS Computational Biology, Public Library of Science, vol. 5(5), pages 1-18, May.
    12. 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.
    13. Maulana, Ardian & Situngkir, Hokky, 2015. "Korelasi Bebas-skala dalam Studi Geo-politik Pemilihan [Scale-free correlation within Geopolitics of Election Studies]," MPRA Paper 66351, University Library of Munich, Germany.
    14. 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.
    15. Hideaki Shimazaki & Shun-ichi Amari & Emery N Brown & Sonja Grün, 2012. "State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-27, March.
    16. 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.
    17. Timothy R Lezon & Ivet Bahar, 2010. "Using Entropy Maximization to Understand the Determinants of Structural Dynamics beyond Native Contact Topology," PLOS Computational Biology, Public Library of Science, vol. 6(6), pages 1-12, June.
    18. Xiaoyuan Liu & Hayato Ushijima-Mwesigwa & Avradip Mandal & Sarvagya Upadhyay & Ilya Safro & Arnab Roy, 2022. "Leveraging special-purpose hardware for local search heuristics," Computational Optimization and Applications, Springer, vol. 82(1), pages 1-29, May.
    19. Sacha Jennifer van Albada & Moritz Helias & Markus Diesmann, 2015. "Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-37, September.
    20. Sahar Gelfman & Quanli Wang & Yi-Fan Lu & Diana Hall & Christopher D Bostick & Ryan Dhindsa & Matt Halvorsen & K Melodi McSweeney & Ellese Cotterill & Tom Edinburgh & Michael A Beaumont & Wayne N Fran, 2018. "meaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-20, October.

    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:pbio00:0060324. 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: plosbiology (email available below). General contact details of provider: https://journals.plos.org/plosbiology/ .

    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.