Nonlinear transient amplification in recurrent neural networks with short-term plasticity

  1. Yue Kris Wu
  2. Friedemann Zenke  Is a corresponding author
  1. Friedrich Miescher Institute for Biomedical Research, Switzerland


To rapidly process information, neural circuits have to amplify specific activity patterns transiently. How the brain performs this nonlinear operation remains elusive. Hebbian assemblies are one possibility whereby strong recurrent excitatory connections boost neuronal activity. However, such Hebbian amplification is often associated with dynamical slowing of network dynamics, non-transient attractor states, and pathological run-away activity. Feedback inhibition can alleviate these effects but typically linearizes responses and reduces amplification gain. Here we study nonlinear transient amplification (NTA), a plausible alternative mechanism that reconciles strong recurrent excitation with rapid amplification while avoiding the above issues. NTA has two distinct temporal phases. Initially, positive feedback excitation selectively amplifies inputs that exceed a critical threshold. Subsequently, short-term plasticity quenches the run-away dynamics into an inhibition-stabilized network state. By characterizing NTA in supralinear network models, we establish that the resulting onset transients are stimulus selective and well-suited for speedy information processing. Further, we find that excitatory-inhibitory co-tuning widens the parameter regime in which NTA is possible in the absence of persistent activity. In summary, NTA provides a parsimonious explanation for how excitatory-inhibitory co-tuning and short-term plasticity collaborate in recurrent networks to achieve transient amplification.

Data availability

This project is a theory project without data.All simulation code has been deposited on GitHub under

The following data sets were generated

Article and author information

Author details

  1. Yue Kris Wu

    Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  2. Friedemann Zenke

    Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1883-644X


Novartis Research Foundation

  • Yue Kris Wu
  • Friedemann Zenke

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Timothy O'Leary, University of Cambridge, United Kingdom

Publication history

  1. Preprint posted: June 10, 2021 (view preprint)
  2. Received: June 14, 2021
  3. Accepted: December 10, 2021
  4. Accepted Manuscript published: December 13, 2021 (version 1)
  5. Version of Record published: February 7, 2022 (version 2)


© 2021, Wu & Zenke

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.


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  1. Yue Kris Wu
  2. Friedemann Zenke
Nonlinear transient amplification in recurrent neural networks with short-term plasticity
eLife 10:e71263.

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    Authors are supported by an NWO-VICI grant (NWO-ZonMW: 016.VICI.170.200 to HT) for HT, LDA, SL, and the Sophia Foundation S18-20, and Erasmus University and Erasmus MC Fellowship for RLM.