IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1008013.html
   My bibliography  Save this article

Linear-nonlinear cascades capture synaptic dynamics

Author

Listed:
  • Julian Rossbroich
  • Daniel Trotter
  • John Beninger
  • Katalin Tóth
  • Richard Naud

Abstract

Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. To model the range of synaptic dynamics observed in experiments, we have developed a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show that it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.Author summary: Understanding how information is transmitted relies heavily on knowledge of the underlying regulatory synaptic dynamics. Existing computational models for capturing such dynamics are often either very complex or too restrictive. As a result, effectively capturing the different types of dynamics observed experimentally remains a challenging problem. Here, we propose a mathematically flexible linear-nonlinear model that is capable of efficiently characterizing synaptic dynamics. We demonstrate the ability of this model to capture different features of experimentally observed data.

Suggested Citation

  • Julian Rossbroich & Daniel Trotter & John Beninger & Katalin Tóth & Richard Naud, 2021. "Linear-nonlinear cascades capture synaptic dynamics," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-27, March.
    • Handle: RePEc:plo:pcbi00:1008013
    DOI: 10.1371/journal.pcbi.1008013
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008013
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008013&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1008013?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. Corinne Teeter & Ramakrishnan Iyer & Vilas Menon & Nathan Gouwens & David Feng & Jim Berg & Aaron Szafer & Nicholas Cain & Hongkui Zeng & Michael Hawrylycz & Christof Koch & Stefan Mihalas, 2018. "Generalized leaky integrate-and-fire models classify multiple neuron types," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
    2. Jonathan W. Pillow & Jonathon Shlens & Liam Paninski & Alexander Sher & Alan M. Litke & E. J. Chichilnisky & Eero P. Simoncelli, 2008. "Spatio-temporal correlations and visual signalling in a complete neuronal population," Nature, Nature, vol. 454(7207), pages 995-999, August.
    3. David J. Herzfeld & Yoshiko Kojima & Robijanto Soetedjo & Reza Shadmehr, 2015. "Encoding of action by the Purkinje cells of the cerebellum," Nature, Nature, vol. 526(7573), pages 439-442, October.
    4. Srdjan Ostojic & Nicolas Brunel, 2011. "From Spiking Neuron Models to Linear-Nonlinear Models," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-16, January.
    5. James M McFarland & Yuwei Cui & Daniel A Butts, 2013. "Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs," PLOS Computational Biology, Public Library of Science, vol. 9(7), pages 1-18, 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. Pengcheng Zhou & Shawn D Burton & Adam C Snyder & Matthew A Smith & Nathaniel N Urban & Robert E Kass, 2015. "Establishing a Statistical Link between Network Oscillations and Neural Synchrony," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-25, October.
    2. Richard Naud & Wulfram Gerstner, 2012. "Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-14, October.
    3. Skander Mensi & Olivier Hagens & Wulfram Gerstner & Christian Pozzorini, 2016. "Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-38, February.
    4. 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.
    5. 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.
    6. Maxim Volgushev & Vladimir Ilin & Ian H Stevenson, 2015. "Identifying and Tracking Simulated Synaptic Inputs from Neuronal Firing: Insights from In Vitro Experiments," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-31, March.
    7. Yuxin Zhang & Yifei Yang & Xiaosi Li & Zijing Yuan & Yuki Todo & Haichuan Yang, 2023. "A Dendritic Neuron Model Optimized by Meta-Heuristics with a Power-Law-Distributed Population Interaction Network for Financial Time-Series Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-20, March.
    8. Cristina Rueda & Itziar Fernández & Yolanda Larriba & Alejandro Rodríguez-Collado, 2021. "The FMM Approach to Analyze Biomedical Signals: Theory, Software, Applications and Future," Mathematics, MDPI, vol. 9(10), pages 1-13, May.
    9. 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.
    10. Jonathan Rubin & Nachum Ulanovsky & Israel Nelken & Naftali Tishby, 2016. "The Representation of Prediction Error in Auditory Cortex," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-28, August.
    11. Omri Harish & David Hansel, 2015. "Asynchronous Rate Chaos in Spiking Neuronal Circuits," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-38, July.
    12. Franklin Leong & Babak Rahmani & Demetri Psaltis & Christophe Moser & Diego Ghezzi, 2024. "An actor-model framework for visual sensory encoding," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    13. Lucas Rudelt & Daniel González Marx & Michael Wibral & Viola Priesemann, 2021. "Embedding optimization reveals long-lasting history dependence in neural spiking activity," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-51, June.
    14. Fanfan Li & Dingwei Li & Chuanqing Wang & Guolei Liu & Rui Wang & Huihui Ren & Yingjie Tang & Yan Wang & Yitong Chen & Kun Liang & Qi Huang & Mohamad Sawan & Min Qiu & Hong Wang & Bowen Zhu, 2024. "An artificial visual neuron with multiplexed rate and time-to-first-spike coding," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    15. François G. C. Blot & Joshua J. White & Amy van Hattem & Licia Scotti & Vaishnavi Balaji & Youri Adolfs & R. Jeroen Pasterkamp & Chris I. De Zeeuw & Martijn Schonewille, 2023. "Purkinje cell microzones mediate distinct kinematics of a single movement," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    16. Johannes Burge & Priyank Jaini, 2017. "Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-32, February.
    17. Kenneth W. Latimer & David J. Freedman, 2023. "Low-dimensional encoding of decisions in parietal cortex reflects long-term training history," Nature Communications, Nature, vol. 14(1), pages 1-24, December.
    18. Braden A W Brinkman & Alison I Weber & Fred Rieke & Eric Shea-Brown, 2016. "How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-34, October.
    19. Jason S Prentice & Olivier Marre & Mark L Ioffe & Adrianna R Loback & Gašper Tkačik & Michael J Berry II, 2016. "Error-Robust Modes of the Retinal Population Code," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-32, November.
    20. Yanyun Ren & Xiaobo Bu & Ming Wang & Yue Gong & Junjie Wang & Yuyang Yang & Guijun Li & Meng Zhang & Ye Zhou & Su-Ting Han, 2022. "Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity," Nature Communications, Nature, vol. 13(1), pages 1-11, 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:pcbi00:1008013. 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.