IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1002133.html
   My bibliography  Save this article

Effective Stimuli for Constructing Reliable Neuron Models

Author

Listed:
  • Shaul Druckmann
  • Thomas K Berger
  • Felix Schürmann
  • Sean Hill
  • Henry Markram
  • Idan Segev

Abstract

The rich dynamical nature of neurons poses major conceptual and technical challenges for unraveling their nonlinear membrane properties. Traditionally, various current waveforms have been injected at the soma to probe neuron dynamics, but the rationale for selecting specific stimuli has never been rigorously justified. The present experimental and theoretical study proposes a novel framework, inspired by learning theory, for objectively selecting the stimuli that best unravel the neuron's dynamics. The efficacy of stimuli is assessed in terms of their ability to constrain the parameter space of biophysically detailed conductance-based models that faithfully replicate the neuron's dynamics as attested by their ability to generalize well to the neuron's response to novel experimental stimuli. We used this framework to evaluate a variety of stimuli in different types of cortical neurons, ages and animals. Despite their simplicity, a set of stimuli consisting of step and ramp current pulses outperforms synaptic-like noisy stimuli in revealing the dynamics of these neurons. The general framework that we propose paves a new way for defining, evaluating and standardizing effective electrical probing of neurons and will thus lay the foundation for a much deeper understanding of the electrical nature of these highly sophisticated and non-linear devices and of the neuronal networks that they compose. Author Summary: Neurons perform complicated non-linear transformations on their input before producing their output - a train of action potentials. This input-output transformation is shaped by the specific composition of ion channels, out of the many possible types, that are embedded in the neuron's membrane. Experimentally, characterizing this transformation relies on injecting different stimuli to the neuron while recording its output; but which of the many possible stimuli should one apply? This combined experimental and theoretical study provides a general theoretical framework for answering this question, examining how different stimuli constrain the space of faithful conductance-based models of the studied neuron. We show that combinations of intracellular step and ramp currents enable the construction of models that both replicate the cell's response and generalize very well to novel stimuli e.g., to “noisy” stimuli mimicking synaptic activity. We experimentally verified our theoretical predictions on several cortical neuron types. This work presents a novel method for reliably linking the microscopic membrane ion channels to the macroscopic electrical behavior of neurons. It provides a much-needed rationale for selecting a particular stimulus set for studying the input-output properties of neurons and paves the way for standardization of experimental protocols along with construction of reliable neuron models.

Suggested Citation

  • Shaul Druckmann & Thomas K Berger & Felix Schürmann & Sean Hill & Henry Markram & Idan Segev, 2011. "Effective Stimuli for Constructing Reliable Neuron Models," PLOS Computational Biology, Public Library of Science, vol. 7(8), pages 1-13, August.
  • Handle: RePEc:plo:pcbi00:1002133
    DOI: 10.1371/journal.pcbi.1002133
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002133
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002133&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1002133?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. Pablo Achard & Erik De Schutter, 2006. "Complex Parameter Landscape for a Complex Neuron Model," PLOS Computational Biology, Public Library of Science, vol. 2(7), pages 1-11, July.
    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. Timothy Rumbell & James Kozloski, 2019. "Dimensions of control for subthreshold oscillations and spontaneous firing in dopamine neurons," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-34, September.
    2. Amrita X Sarkar & Eric A Sobie, 2010. "Regression Analysis for Constraining Free Parameters in Electrophysiological Models of Cardiac Cells," PLOS Computational Biology, Public Library of Science, vol. 6(9), pages 1-11, September.
    3. Dimitrios V Vavoulis & Volko A Straub & John A D Aston & Jianfeng Feng, 2012. "A Self-Organizing State-Space-Model Approach for Parameter Estimation in Hodgkin-Huxley-Type Models of Single Neurons," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-1, March.
    4. Mustafa Baz & Brady Hunsaker & Oleg Prokopyev, 2011. "How much do we “pay” for using default parameters?," Computational Optimization and Applications, Springer, vol. 48(1), pages 91-108, January.
    5. Quentin J M Huys & Liam Paninski, 2009. "Smoothing of, and Parameter Estimation from, Noisy Biophysical Recordings," PLOS Computational Biology, Public Library of Science, vol. 5(5), pages 1-16, May.
    6. Xu, Ying & Guo, Yeye & Ren, Guodong & Ma, Jun, 2020. "Dynamics and stochastic resonance in a thermosensitive neuron," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    7. Matthew Plumlee & V. Roshan Joseph & Hui Yang, 2016. "Calibrating Functional Parameters in the Ion Channel Models of Cardiac Cells," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 500-509, April.
    8. Willemijn Groenendaal & Francis A Ortega & Armen R Kherlopian & Andrew C Zygmunt & Trine Krogh-Madsen & David J Christini, 2015. "Cell-Specific Cardiac Electrophysiology Models," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-22, April.

    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:pcbi00:1002133. 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.