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

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.

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.

Metrics

  • 1,097
    views
  • 215
    downloads
  • 17
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Simon Schug
  2. Frederik Benzing
  3. Angelika Steger
(2021)
Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma
eLife 10:e69884.
https://doi.org/10.7554/eLife.69884

Share this article

https://doi.org/10.7554/eLife.69884

Further reading

    1. Neuroscience
    Hans Martin Kjer, Mariam Andersson ... Tim B Dyrby
    Research Article

    We used diffusion MRI and x-ray synchrotron imaging on monkey and mice brains to examine the organisation of fibre pathways in white matter across anatomical scales. We compared the structure in the corpus callosum and crossing fibre regions and investigated the differences in cuprizone-induced demyelination in mouse brains versus healthy controls. Our findings revealed common principles of fibre organisation that apply despite the varying patterns observed across species; small axonal fasciculi and major bundles formed laminar structures with varying angles, according to the characteristics of major pathways. Fasciculi exhibited non-straight paths around obstacles like blood vessels, comparable across the samples of varying fibre complexity and demyelination. Quantifications of fibre orientation distributions were consistent across anatomical length scales and modalities, whereas tissue anisotropy had a more complex relationship, both dependent on the field-of-view. Our study emphasises the need to balance field-of-view and voxel size when characterising white matter features across length scales.

    1. Neuroscience
    Aneri Soni, Michael J Frank
    Research Article

    How and why is working memory (WM) capacity limited? Traditional cognitive accounts focus either on limitations on the number or items that can be stored (slots models), or loss of precision with increasing load (resource models). Here, we show that a neural network model of prefrontal cortex and basal ganglia can learn to reuse the same prefrontal populations to store multiple items, leading to resource-like constraints within a slot-like system, and inducing a trade-off between quantity and precision of information. Such ‘chunking’ strategies are adapted as a function of reinforcement learning and WM task demands, mimicking human performance and normative models. Moreover, adaptive performance requires a dynamic range of dopaminergic signals to adjust striatal gating policies, providing a new interpretation of WM difficulties in patient populations such as Parkinson’s disease, ADHD, and schizophrenia. These simulations also suggest a computational rather than anatomical limit to WM capacity.