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Activity-dependent scaling of quantal amplitude in neocortical neurons

Author

Listed:
  • Gina G. Turrigiano

    (Brandeis University)

  • Kenneth R. Leslie

    (Brandeis University)

  • Niraj S. Desai

    (Brandeis University)

  • Lana C. Rutherford

    (Brandeis University)

  • Sacha B. Nelson

    (Brandeis University)

Abstract

Information is stored in neural circuits through long-lasting changes in synaptic strengths1,2. Most studies of information storage have focused on mechanisms such as long-term potentiation and depression (LTP and LTD), in which synaptic strengths change in a synapse-specific manner3,4. In contrast, little attention has been paid to mechanisms that regulate the total synaptic strength of a neuron. Here we describe a new form of synaptic plasticity that increases or decreases the strength of all of a neuron's synaptic inputs as a function of activity. Chronic blockade of cortical culture activity increased the amplitude of miniature excitatory postsynaptic currents (mEPSCs) without changing their kinetics. Conversely, blocking GABA (γ-aminutyric acid)-mediated inhibition initially raised firing rates, but over a 48-hour period mESPC amplitudes decreased and firing rates returned to close to control values. These changes were at least partly due to postsynaptic alterations in the response to glutamate, and apparently affected each synapse in proportion to its initial strength. Such ‘synaptic scaling’ may help to ensure that firing rates do not become saturated during developmental changes in the number and strength of synaptic inputs5, as well as stabilizing synaptic strengths during Hebbian modification6,7 and facilitating competition between synapses7,8,9.

Suggested Citation

  • Gina G. Turrigiano & Kenneth R. Leslie & Niraj S. Desai & Lana C. Rutherford & Sacha B. Nelson, 1998. "Activity-dependent scaling of quantal amplitude in neocortical neurons," Nature, Nature, vol. 391(6670), pages 892-896, February.
    • Handle: RePEc:nat:nature:v:391:y:1998:i:6670:d:10.1038_36103
    DOI: 10.1038/36103
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    Citations

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    Cited by:

    1. Christian Keck & Cristina Savin & Jörg Lücke, 2012. "Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin?," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-15, March.
    2. Giorgia Dellaferrera & Stanisław Woźniak & Giacomo Indiveri & Angeliki Pantazi & Evangelos Eleftheriou, 2022. "Introducing principles of synaptic integration in the optimization of deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. John Palmer & Adam Keane & Pulin Gong, 2017. "Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-23, July.
    4. Maxime Lemieux & Narges Karimi & Frederic Bretzner, 2024. "Functional plasticity of glutamatergic neurons of medullary reticular nuclei after spinal cord injury in mice," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. Iris Reuveni & Sourav Ghosh & Edi Barkai, 2017. "Real Time Multiplicative Memory Amplification Mediated by Whole-Cell Scaling of Synaptic Response in Key Neurons," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-31, January.
    6. Damien M O’Halloran, 2020. "Simulation model of CA1 pyramidal neurons reveal opposing roles for the Na+/Ca2+ exchange current and Ca2+-activated K+ current during spike-timing dependent synaptic plasticity," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-12, March.
    7. Tiziano D’Albis & Richard Kempter, 2017. "A single-cell spiking model for the origin of grid-cell patterns," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-41, October.
    8. Sacha Jennifer van Albada & Moritz Helias & Markus Diesmann, 2015. "Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-37, September.
    9. Matteo Saponati & Martin Vinck, 2023. "Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    10. Aseel Shomar & Lukas Geyrhofer & Noam E Ziv & Naama Brenner, 2017. "Cooperative stochastic binding and unbinding explain synaptic size dynamics and statistics," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-24, July.
    11. Pierre Yger & Kenneth D Harris, 2013. "The Convallis Rule for Unsupervised Learning in Cortical Networks," PLOS Computational Biology, Public Library of Science, vol. 9(10), pages 1-16, October.
    12. Mizusaki, Beatriz E.P. & Agnes, Everton J. & Erichsen, Rubem & Brunnet, Leonardo G., 2017. "Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 279-286.
    13. Niranjan Chakravarthy & Shivkumar Sabesan & Kostas Tsakalis & Leon Iasemidis, 2009. "Controlling epileptic seizures in a neural mass model," Journal of Combinatorial Optimization, Springer, vol. 17(1), pages 98-116, January.
    14. Angulo-Garcia, David & Torcini, Alessandro, 2014. "Stable chaos in fluctuation driven neural circuits," Chaos, Solitons & Fractals, Elsevier, vol. 69(C), pages 233-245.
    15. Chiara Bartolozzi & Giacomo Indiveri & Elisa Donati, 2022. "Embodied neuromorphic intelligence," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    16. Juan Prada & Manju Sasi & Corinna Martin & Sibylle Jablonka & Thomas Dandekar & Robert Blum, 2018. "An open source tool for automatic spatiotemporal assessment of calcium transients and local ‘signal-close-to-noise’ activity in calcium imaging data," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-34, March.
    17. Kendra S Burbank, 2015. "Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-25, December.
    18. Jannis Born & Juan M Galeazzi & Simon M Stringer, 2017. "Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-35, May.

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