IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1000886.html
   My bibliography  Save this article

Stochastic Ion Channel Gating in Dendritic Neurons: Morphology Dependence and Probabilistic Synaptic Activation of Dendritic Spikes

Author

Listed:
  • Robert C Cannon
  • Cian O'Donnell
  • Matthew F Nolan

Abstract

Neuronal activity is mediated through changes in the probability of stochastic transitions between open and closed states of ion channels. While differences in morphology define neuronal cell types and may underlie neurological disorders, very little is known about influences of stochastic ion channel gating in neurons with complex morphology. We introduce and validate new computational tools that enable efficient generation and simulation of models containing stochastic ion channels distributed across dendritic and axonal membranes. Comparison of five morphologically distinct neuronal cell types reveals that when all simulated neurons contain identical densities of stochastic ion channels, the amplitude of stochastic membrane potential fluctuations differs between cell types and depends on sub-cellular location. For typical neurons, the amplitude of membrane potential fluctuations depends on channel kinetics as well as open probability. Using a detailed model of a hippocampal CA1 pyramidal neuron, we show that when intrinsic ion channels gate stochastically, the probability of initiation of dendritic or somatic spikes by dendritic synaptic input varies continuously between zero and one, whereas when ion channels gate deterministically, the probability is either zero or one. At physiological firing rates, stochastic gating of dendritic ion channels almost completely accounts for probabilistic somatic and dendritic spikes generated by the fully stochastic model. These results suggest that the consequences of stochastic ion channel gating differ globally between neuronal cell-types and locally between neuronal compartments. Whereas dendritic neurons are often assumed to behave deterministically, our simulations suggest that a direct consequence of stochastic gating of intrinsic ion channels is that spike output may instead be a probabilistic function of patterns of synaptic input to dendrites.Author Summary: The activity of neurons in the brain is mediated through changes in the probability of random transitions between open and closed states of ion channels. Since differences in morphology define distinct types of neuron and may underlie neurological disorders, it is important to understand how morphology influences the functional consequences of these random transitions. However, the complexities of neuronal morphology, together with the large number of ion channels expressed by a single neuron, have made this issue difficult to explore systematically. We introduce and validate new computational tools that enable efficient generation and simulation of models containing ion channels distributed across complex neuronal morphologies. Using these tools we demonstrate that the impact of random ion channel opening depends on neuronal morphology and ion channel kinetics. We show that in a realistic model of a neuron important for navigation and memory random gating of ion channels substantially modifies responses to synaptic input. Our results suggest a new and general perspective, whereby output from a neuron is a probabilistic rather than a fixed function of synaptic input to its dendrites. These results and new tools will contribute to the understanding of how intrinsic properties of neurons influence computations carried out within the brain.

Suggested Citation

  • Robert C Cannon & Cian O'Donnell & Matthew F Nolan, 2010. "Stochastic Ion Channel Gating in Dendritic Neurons: Morphology Dependence and Probabilistic Synaptic Activation of Dendritic Spikes," PLOS Computational Biology, Public Library of Science, vol. 6(8), pages 1-18, August.
  • Handle: RePEc:plo:pcbi00:1000886
    DOI: 10.1371/journal.pcbi.1000886
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000886
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000886&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1000886?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. Robert C Cannon & Giampaolo D'Alessandro, 2006. "The Ion Channel Inverse Problem: Neuroinformatics Meets Biophysics," PLOS Computational Biology, Public Library of Science, vol. 2(8), pages 1-8, August.
    2. Antti Saarinen & Marja-Leena Linne & Olli Yli-Harja, 2008. "Stochastic Differential Equation Model for Cerebellar Granule Cell Excitability," PLOS Computational Biology, Public Library of Science, vol. 4(2), pages 1-11, February.
    3. Dax A. Hoffman & Jeffrey C. Magee & Costa M. Colbert & Daniel Johnston, 1997. "K+ channel regulation of signal propagation in dendrites of hippocampal pyramidal neurons," Nature, Nature, vol. 387(6636), pages 869-875, June.
    4. Joshua T Dudman & Matthew F Nolan, 2009. "Stochastically Gating Ion Channels Enable Patterned Spike Firing through Activity-Dependent Modulation of Spike Probability," PLOS Computational Biology, Public Library of Science, vol. 5(2), pages 1-20, February.
    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. Katri Hituri & Marja-Leena Linne, 2013. "Comparison of Models for IP3 Receptor Kinetics Using Stochastic Simulations," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-13, April.
    2. Marc-Oliver Gewaltig & Robert Cannon, 2014. "Current Practice in Software Development for Computational Neuroscience and How to Improve It," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-9, 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. Liu, Yaru & Liu, Shenquan & Zhan, Feibiao & Zhang, Xiaohan, 2020. "Firing patterns of the modified Hodgkin–Huxley models subject to Taylor ’s formula," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    2. Pietro Balbi & Paolo Massobrio & Jeanette Hellgren Kotaleski, 2017. "A single Markov-type kinetic model accounting for the macroscopic currents of all human voltage-gated sodium channel isoforms," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-29, September.
    3. Songting Li & Nan Liu & Xiao-hui Zhang & Douglas Zhou & David Cai, 2014. "Bilinearity in Spatiotemporal Integration of Synaptic Inputs," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-15, December.
    4. Wolfgang Maass & Prashant Joshi & Eduardo D Sontag, 2007. "Computational Aspects of Feedback in Neural Circuits," PLOS Computational Biology, Public Library of Science, vol. 3(1), pages 1-20, January.
    5. Andrey R Stepanyuk & Anya L Borisyuk & Pavel V Belan, 2011. "Efficient Maximum Likelihood Estimation of Kinetic Rate Constants from Macroscopic Currents," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-18, December.
    6. Zhenrui Liao & Kevin C. Gonzalez & Deborah M. Li & Catalina M. Yang & Donald Holder & Natalie E. McClain & Guofeng Zhang & Stephen W. Evans & Mariya Chavarha & Jane Simko & Christopher D. Makinson & M, 2024. "Functional architecture of intracellular oscillations in hippocampal dendrites," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    7. Tuckwell, Henry C. & Jost, Jürgen, 2012. "Analysis of inverse stochastic resonance and the long-term firing of Hodgkin–Huxley neurons with Gaussian white noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5311-5325.
    8. Pojeong Park & J. David Wong-Campos & Daniel G. Itkis & Byung Hun Lee & Yitong Qi & Hunter C. Davis & Benjamin Antin & Amol Pasarkar & Jonathan B. Grimm & Sarah E. Plutkis & Katie L. Holland & Liam Pa, 2025. "Dendritic excitations govern back-propagation via a spike-rate accelerometer," Nature Communications, Nature, vol. 16(1), pages 1-20, December.
    9. Daniele Linaro & Marco Storace & Michele Giugliano, 2011. "Accurate and Fast Simulation of Channel Noise in Conductance-Based Model Neurons by Diffusion Approximation," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-17, March.
    10. Tilman Kispersky & John A White & Horacio G Rotstein, 2010. "The Mechanism of Abrupt Transition between Theta and Hyper-Excitable Spiking Activity in Medial Entorhinal Cortex Layer II Stellate Cells," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-21, November.
    11. Katri Hituri & Marja-Leena Linne, 2013. "Comparison of Models for IP3 Receptor Kinetics Using Stochastic Simulations," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-13, April.
    12. I.B., Tagne nkounga & F.M., Moukam kakmeni & R., Yamapi, 2022. "Birhythmic oscillations and global stability analysis of a conductance-based neuronal model under ion channel fluctuations," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    13. Joshua H Goldwyn & Eric Shea-Brown, 2011. "The What and Where of Adding Channel Noise to the Hodgkin-Huxley Equations," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-9, November.
    14. Anca R Radulescu, 2010. "Mechanisms Explaining Transitions between Tonic and Phasic Firing in Neuronal Populations as Predicted by a Low Dimensional Firing Rate Model," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-14, September.
    15. Meron Gurkiewicz & Alon Korngreen, 2007. "A Numerical Approach to Ion Channel Modelling Using Whole-Cell Voltage-Clamp Recordings and a Genetic Algorithm," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-15, August.
    16. Joshua T Dudman & Matthew F Nolan, 2009. "Stochastically Gating Ion Channels Enable Patterned Spike Firing through Activity-Dependent Modulation of Spike Probability," PLOS Computational Biology, Public Library of Science, vol. 5(2), pages 1-20, February.
    17. Michele Migliore & Rosanna Migliore, 2012. "Know Your Current Ih: Interaction with a Shunting Current Explains the Puzzling Effects of Its Pharmacological or Pathological Modulations," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-8, May.
    18. Francisco J H Heras & Mikko Vähäsöyrinki & Jeremy E Niven, 2018. "Modulation of voltage-dependent K+ conductances in photoreceptors trades off investment in contrast gain for bandwidth," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-33, November.
    19. Nkounga, I.B. Tagne & Xia, Yibo & Yanchuk, Serhiy & Yamapi, R. & Kurths, Jürgen, 2023. "Generalized FitzHugh–Nagumo model with tristable dynamics: Deterministic and stochastic bifurcations," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

    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:pcbi00:1000886. 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.