IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003206.html
   My bibliography  Save this article

Spike Triggered Covariance in Strongly Correlated Gaussian Stimuli

Author

Listed:
  • Johnatan Aljadeff
  • Ronen Segev
  • Michael J Berry II
  • Tatyana O Sharpee

Abstract

Many biological systems perform computations on inputs that have very large dimensionality. Determining the relevant input combinations for a particular computation is often key to understanding its function. A common way to find the relevant input dimensions is to examine the difference in variance between the input distribution and the distribution of inputs associated with certain outputs. In systems neuroscience, the corresponding method is known as spike-triggered covariance (STC). This method has been highly successful in characterizing relevant input dimensions for neurons in a variety of sensory systems. So far, most studies used the STC method with weakly correlated Gaussian inputs. However, it is also important to use this method with inputs that have long range correlations typical of the natural sensory environment. In such cases, the stimulus covariance matrix has one (or more) outstanding eigenvalues that cannot be easily equalized because of sampling variability. Such outstanding modes interfere with analyses of statistical significance of candidate input dimensions that modulate neuronal outputs. In many cases, these modes obscure the significant dimensions. We show that the sensitivity of the STC method in the regime of strongly correlated inputs can be improved by an order of magnitude or more. This can be done by evaluating the significance of dimensions in the subspace orthogonal to the outstanding mode(s). Analyzing the responses of retinal ganglion cells probed with Gaussian noise, we find that taking into account outstanding modes is crucial for recovering relevant input dimensions for these neurons.Author Summary: In many areas of computational biology, including the analyses of genetic mutations, protein stability and neural coding, as well as in economics, one of the most basic and important steps of data analysis is to find the relevant input dimensions for a particular task. In neural coding problems, the spike-triggered covariance (STC) method identifies relevant input dimensions by comparing the variance of the input distribution along different dimensions to the variance of inputs that elicited a neural response. While in theory the method can be applied to Gaussian stimuli with or without correlations, it has so far been used in studies with only weakly correlated stimuli. Here we show that to use STC with strongly correlated, -type inputs, one has to take into account that the covariance matrix of random samples from this distribution has a complex structure, with one or more outstanding modes. We use simulations on model neurons as well as an analysis of the responses of retinal neurons to demonstrate that taking the presence of these outstanding modes into account improves the sensitivity of the STC method by more than an order of magnitude.

Suggested Citation

  • Johnatan Aljadeff & Ronen Segev & Michael J Berry II & Tatyana O Sharpee, 2013. "Spike Triggered Covariance in Strongly Correlated Gaussian Stimuli," PLOS Computational Biology, Public Library of Science, vol. 9(9), pages 1-12, September.
    • Handle: RePEc:plo:pcbi00:1003206
    DOI: 10.1371/journal.pcbi.1003206
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003206
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003206&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1003206?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. Toshihiko Hosoya & Stephen A. Baccus & Markus Meister, 2005. "Dynamic predictive coding by the retina," Nature, Nature, vol. 436(7047), pages 71-77, July.
    2. Gidon Felsen & Jon Touryan & Feng Han & Yang Dan, 2005. "Cortical Sensitivity to Visual Features in Natural Scenes," PLOS Biology, Public Library of Science, vol. 3(10), pages 1-1, September.
    3. Tatyana O. Sharpee & Hiroki Sugihara & Andrei V. Kurgansky & Sergei P. Rebrik & Michael P. Stryker & Kenneth D. Miller, 2006. "Adaptive filtering enhances information transmission in visual cortex," Nature, Nature, vol. 439(7079), pages 936-942, February.
    4. Miguel Maravall & Rasmus S Petersen & Adrienne L Fairhall & Ehsan Arabzadeh & Mathew E Diamond, 2007. "Shifts in Coding Properties and Maintenance of Information Transmission during Adaptation in Barrel Cortex," PLOS Biology, Public Library of Science, vol. 5(2), pages 1-12, January.
    5. Jeffrey D Fitzgerald & Ryan J Rowekamp & Lawrence C Sincich & Tatyana O Sharpee, 2011. "Second Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-9, October.
    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. Niru Maheswaranathan & David B Kastner & Stephen A Baccus & Surya Ganguli, 2018. "Inferring hidden structure in multilayered neural circuits," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-30, August.

    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. Jian K Liu & Tim Gollisch, 2015. "Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-30, July.
    2. Jeffrey D Fitzgerald & Ryan J Rowekamp & Lawrence C Sincich & Tatyana O Sharpee, 2011. "Second Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-9, October.
    3. Miguel Maravall & Rasmus S Petersen & Adrienne L Fairhall & Ehsan Arabzadeh & Mathew E Diamond, 2007. "Shifts in Coding Properties and Maintenance of Information Transmission during Adaptation in Barrel Cortex," PLOS Biology, Public Library of Science, vol. 5(2), pages 1-12, January.
    4. Marcus H C Howlett & Robert G Smith & Maarten Kamermans, 2017. "A novel mechanism of cone photoreceptor adaptation," PLOS Biology, Public Library of Science, vol. 15(4), pages 1-28, April.
    5. Klaus Wimmer & K Jannis Hildebrandt & R Matthias Hennig & Klaus Obermayer, 2008. "Adaptation and Selective Information Transmission in the Cricket Auditory Neuron AN2," PLOS Computational Biology, Public Library of Science, vol. 4(9), pages 1-18, September.
    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. Federico Bolaños & Javier G. Orlandi & Ryo Aoki & Akshay V. Jagadeesh & Justin L. Gardner & Andrea Benucci, 2024. "Efficient coding of natural images in the mouse visual cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    8. Matthias S Keil & Agata Lapedriza & David Masip & Jordi Vitria, 2008. "Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine," PLOS ONE, Public Library of Science, vol. 3(7), pages 1-5, July.
    9. Nicholas A Lesica & Toshiyuki Ishii & Garrett B Stanley & Toshihiko Hosoya, 2008. "Estimating Receptive Fields from Responses to Natural Stimuli with Asymmetric Intensity Distributions," PLOS ONE, Public Library of Science, vol. 3(8), pages 1-10, August.
    10. Matthias S Keil, 2009. "“I Look in Your Eyes, Honey”: Internal Face Features Induce Spatial Frequency Preference for Human Face Processing," PLOS Computational Biology, Public Library of Science, vol. 5(3), pages 1-13, March.
    11. Krishnamurthy V. Vemuru, 2022. "Implementation of the Canny Edge Detector Using a Spiking Neural Network," Future Internet, MDPI, vol. 14(12), pages 1-12, December.
    12. Gabriel D Puccini & Albert Compte & Miguel Maravall, 2006. "Stimulus Dependence of Barrel Cortex Directional Selectivity," PLOS ONE, Public Library of Science, vol. 1(1), pages 1-6, December.
    13. Tristan G. Heintz & Antonio J. Hinojosa & Sina E. Dominiak & Leon Lagnado, 2022. "Opposite forms of adaptation in mouse visual cortex are controlled by distinct inhibitory microcircuits," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    14. Omer Mano & Damon A Clark, 2017. "Graphics Processing Unit-Accelerated Code for Computing Second-Order Wiener Kernels and Spike-Triggered Covariance," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-11, January.
    15. Jerome Carriot & Graham McAllister & Hamed Hooshangnejad & Isabelle Mackrous & Kathleen E. Cullen & Maurice J. Chacron, 2022. "Sensory adaptation mediates efficient and unambiguous encoding of natural stimuli by vestibular thalamocortical pathways," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    16. Lucas Theis & Andrè Maia Chagas & Daniel Arnstein & Cornelius Schwarz & Matthias Bethge, 2013. "Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-9, November.
    17. Benjamin R Cowley & Matthew A Smith & Adam Kohn & Byron M Yu, 2016. "Stimulus-Driven Population Activity Patterns in Macaque Primary Visual Cortex," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-31, December.
    18. Ross S Williamson & Maneesh Sahani & Jonathan W Pillow, 2015. "The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-31, April.
    19. Gabriel D Puccini & Maria V Sanchez-Vives & Albert Compte, 2007. "Integrated Mechanisms of Anticipation and Rate-of-Change Computations in Cortical Circuits," PLOS Computational Biology, Public Library of Science, vol. 3(5), pages 1-13, May.
    20. Matthew F. Tang & Ehsan Kheradpezhouh & Conrad C. Y. Lee & J. Edwin Dickinson & Jason B. Mattingley & Ehsan Arabzadeh, 2023. "Expectation violations enhance neuronal encoding of sensory information in mouse primary visual cortex," Nature Communications, Nature, vol. 14(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:pcbi00:1003206. 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.