1. Neuroscience
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Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma

  1. Simon Schug  Is a corresponding author
  2. Frederik Benzing
  3. Angelika Steger
  1. University of Zurich and ETH Zurich, Switzerland
  2. ETH Zurich, Switzerland
Research Article
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Cite this article as: eLife 2021;10:e69884 doi: 10.7554/eLife.69884

Abstract

When an action potential arrives at a synapse there is a large probability that no neurotransmitter is released. Surprisingly, simple computational models suggest that these synaptic failures enable information processing at lower metabolic costs. However, these models only consider information transmission at single synapses ignoring the remainder of the neural network as well as its overall computational goal. Here, we investigate how synaptic failures affect the energy efficiency of models of entire neural networks that solve a goal-driven task. We find that presynaptic stochasticity and plasticity improve energy efficiency and show that the network allocates most energy to a sparse subset of important synapses. We demonstrate that stabilising these synapses helps to alleviate the stability-plasticity dilemma, thus connecting a presynaptic notion of importance to a computational role in lifelong learning. Overall, our findings present a set of hypotheses for how presynaptic plasticity and stochasticity contribute to sparsity, energy efficiency and improved trade-offs in the stability-plasticity dilemma.

Data availability

Code for experiments is part of the submission and is published on GitHub (https://github.com/smonsays/presynaptic-stochasticity).

The following previously published data sets were used

Article and author information

Author details

  1. Simon Schug

    Institute of Neuroinformatics,, University of Zurich and ETH Zurich, Zurich, Switzerland
    For correspondence
    sschug@ethz.ch
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5305-2547
  2. Frederik Benzing

    Department of Computer Science, ETH Zurich, Zurich, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  3. Angelika Steger

    Department of Computer Science, ETH Zurich, Zurich, Switzerland
    Competing interests
    The authors declare that no competing interests exist.

Funding

Swiss National Science Foundation (Ambizione Grant,PZ00P318602)

  • Simon Schug

Swiss National Science Foundation (CRSII5_173721)

  • Frederik Benzing
  • Angelika Steger

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. Received: April 28, 2021
  2. Accepted: October 18, 2021
  3. Accepted Manuscript published: October 18, 2021 (version 1)

Copyright

© 2021, Schug et al.

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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