Optimal compensation for neuron loss

  1. David TG Barrett
  2. Sophie Denève
  3. Christian K Machens  Is a corresponding author
  1. University of Cambridge, United Kingdom
  2. École Normale Supérieure, France
  3. Champalimaud Centre for the Unknown, Portugal

Abstract

The brain has an impressive ability to withstand neural damage. Diseases that kill neurons can go unnoticed for years, and incomplete brain lesions or silencing of neurons often fail to produce any behavioral effect. How does the brain compensate for such damage, and what are the limits of this compensation? We propose that neural circuits immediately compensate for neuron loss, thereby preserving their function as much as possible. We show that this compensation can explain changes in tuning curves induced by neuron silencing across a variety of systems, including the primary visual cortex. We find that compensatory mechanisms can be implemented through the dynamics of networks with a tight balance of excitation and inhibition, without requiring synaptic plasticity. The limits of this compensatory mechanism are reached when excitation and inhibition become unbalanced, thereby demarcating a recovery boundary, where signal representation fails and where diseases may become symptomatic.

Article and author information

Author details

  1. David TG Barrett

    Department of Engineering, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Sophie Denève

    Laboratoire de Neurosciences Cognitives, École Normale Supérieure, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  3. Christian K Machens

    Champalimaud Centre for the Unknown, Lisbon, Portugal
    For correspondence
    christian.machens@neuro.fchampalimaud.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1717-1562

Funding

Deutsche Forschungsgemeinschaft (Emmy-Noether)

  • Christian K Machens

Agence Nationale de Recherche (Chaire d'Excellence)

  • Christian K Machens

James McDonnell Foundation

  • Christian K Machens

European Research Council (ERC FP7-PREDSPIKE)

  • Christian K Machens

European Research Council (BIND MECT-CT-20095-024831)

  • Christian K Machens

European Research Council (BACS 796 FP6-IST-027140)

  • Christian K Machens

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

Reviewing Editor

  1. Frances K Skinner, University Health Network, Canada

Version history

  1. Received: October 20, 2015
  2. Accepted: December 8, 2016
  3. Accepted Manuscript published: December 9, 2016 (version 1)
  4. Version of Record published: January 31, 2017 (version 2)

Copyright

© 2016, Barrett 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

  • 2,577
    views
  • 653
    downloads
  • 27
    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. David TG Barrett
  2. Sophie Denève
  3. Christian K Machens
(2016)
Optimal compensation for neuron loss
eLife 5:e12454.
https://doi.org/10.7554/eLife.12454

Share this article

https://doi.org/10.7554/eLife.12454

Further reading

    1. Neuroscience
    Vezha Boboeva, Alberto Pezzotta ... Athena Akrami
    Research Article

    The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased toward the average of past observations. It is assumed to be an optimal strategy by the brain and commonly thought of as an expression of the brain’s ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience – producing short-term sensory history biases – naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli and are not due to a gradual shift of the memory toward the sensory distribution’s mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

    1. Neuroscience
    Michael Berger, Michèle Fraatz ... Henrike Scholz
    Research Article

    The brain regulates food intake in response to internal energy demands and food availability. However, can internal energy storage influence the type of memory that is formed? We show that the duration of starvation determines whether Drosophila melanogaster forms appetitive short-term or longer-lasting intermediate memories. The internal glycogen storage in the muscles and adipose tissue influences how intensely sucrose-associated information is stored. Insulin-like signaling in octopaminergic reward neurons integrates internal energy storage into memory formation. Octopamine, in turn, suppresses the formation of long-term memory. Octopamine is not required for short-term memory because octopamine-deficient mutants can form appetitive short-term memory for sucrose and to other nutrients depending on the internal energy status. The reduced positive reinforcing effect of sucrose at high internal glycogen levels, combined with the increased stability of food-related memories due to prolonged periods of starvation, could lead to increased food intake.