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Linear-nonlinear cascades capture synaptic dynamics

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
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