Stable task information from an unstable neural population

  1. Michael E Rule
  2. Adrianna R Loback
  3. Dhruva Raman
  4. Laura N Driscoll
  5. Christopher D Harvey
  6. Timothy O'Leary  Is a corresponding author
  1. University of Cambridge, United Kingdom
  2. Stanford University, United States
  3. Harvard Medical School, United States

Abstract

Over days and weeks, neural activity representing an animal's position and movement in sensorimotor cortex has been found to continually reconfigure or 'drift' during repeated trials of learned tasks, with no obvious change in behavior. This challenges classical theories which assume stable engrams underlie stable behavior. However, it is not known whether this drift occurs systematically, allowing downstream circuits to extract consistent information. Analyzing long-term calcium imaging recordings from posterior parietal cortex in mice (Mus musculus), we show that drift is systematically constrained far above chance, facilitating a linear weighted readout of behavioural variables. However, a significant component of drift continually degrades a fixed readout, implying that drift is not confined to a null coding space. We calculate the amount of plasticity required to compensate drift independently of any learning rule, and find that this is within physiologically achievable bounds. We demonstrate that a simple, biologically plausible local learning rule can achieve these bounds, accurately decoding behavior over many days.

Data availability

Datasets recorded in Driscoll et al., 2017, are available from the Dryad repository under the doi:/10.5061/dryad.gqnk98sjq. The analysis code generated during this study is available on Github github.com/michaelerule/stable-task-information.

The following data sets were generated

Article and author information

Author details

  1. Michael E Rule

    Department of Engineering, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4196-774X
  2. Adrianna R Loback

    Department of Engineering, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Dhruva Raman

    Department of Engineering, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Laura N Driscoll

    Electrical Engineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Christopher D Harvey

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Timothy O'Leary

    Engineering, University of Cambridge, Cambridge, United Kingdom
    For correspondence
    tso24@cam.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1029-0158

Funding

Human Frontier Science Program (RGY0069)

  • Michael E Rule
  • Adrianna R Loback
  • Christopher D Harvey
  • Timothy O'Leary

H2020 European Research Council (FLEXNEURO 716643)

  • Dhruva Raman
  • Timothy O'Leary

National Institutes of Health (NS089521)

  • Christopher D Harvey

National Institutes of Health (MH107620)

  • Christopher D Harvey

National Institutes of Health (NS108410)

  • Christopher D Harvey

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

Reviewing Editor

  1. Stephanie Palmer, University of Chicago, United States

Publication history

  1. Received: August 15, 2019
  2. Accepted: June 17, 2020
  3. Accepted Manuscript published: July 14, 2020 (version 1)
  4. Version of Record published: July 30, 2020 (version 2)

Copyright

© 2020, Rule 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

  • 5,430
    Page views
  • 896
    Downloads
  • 28
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

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. Michael E Rule
  2. Adrianna R Loback
  3. Dhruva Raman
  4. Laura N Driscoll
  5. Christopher D Harvey
  6. Timothy O'Leary
(2020)
Stable task information from an unstable neural population
eLife 9:e51121.
https://doi.org/10.7554/eLife.51121

Further reading

    1. Computational and Systems Biology
    Robert West, Hadrien Delattre ... Orkun S Soyer
    Research Article Updated

    Cycling of co-substrates, whereby a metabolite is converted among alternate forms via different reactions, is ubiquitous in metabolism. Several cycled co-substrates are well known as energy and electron carriers (e.g. ATP and NAD(P)H), but there are also other metabolites that act as cycled co-substrates in different parts of central metabolism. Here, we develop a mathematical framework to analyse the effect of co-substrate cycling on metabolic flux. In the cases of a single reaction and linear pathways, we find that co-substrate cycling imposes an additional flux limit on a reaction, distinct to the limit imposed by the kinetics of the primary enzyme catalysing that reaction. Using analytical methods, we show that this additional limit is a function of the total pool size and turnover rate of the cycled co-substrate. Expanding from this insight and using simulations, we show that regulation of these two parameters can allow regulation of flux dynamics in branched and coupled pathways. To support these theoretical insights, we analysed existing flux measurements and enzyme levels from the central carbon metabolism and identified several reactions that could be limited by the dynamics of co-substrate cycling. We discuss how the limitations imposed by co-substrate cycling provide experimentally testable hypotheses on specific metabolic phenotypes. We conclude that measuring and controlling co-substrate dynamics is crucial for understanding and engineering metabolic fluxes in cells.

    1. Computational and Systems Biology
    2. Neuroscience
    Hossein Shahabi, Dileep R Nair, Richard M Leahy
    Research Article

    Seizure generation, propagation, and termination occur through spatiotemporal brain networks. In this paper, we demonstrate the significance of large-scale brain interactions in high-frequency (80-200 Hz) for identification of the epileptogenic zone (EZ) and seizure evolution. To incorporate the continuity of neural dynamics, here we have modeled brain connectivity constructed from stereoelectroencephalography (SEEG) data during seizures using multilayer networks. After introducing a new measure of brain connectivity for temporal networks, named multilayer eigenvector centrality (mlEVC), we applied a consensus hierarchical clustering on the developed model to identify the epileptogenic zone (EZ) as a cluster of nodes with distinctive brain connectivity in the ictal period. Our algorithm could successfully predict electrodes inside the resected volume as EZ for 88% of participants, who all were seizure-free for at least 12 months after surgery. Our findings illustrated significant and unique desynchronization between EZ and the rest of the brain in early to mid-seizure. We showed that aging and duration of epilepsy intensify this desynchronization, which can be the outcome of abnormal neuroplasticity. Additionally, we illustrated that seizures evolve with various network topologies, confirming the existence of different epileptogenic networks in each patient. Our findings suggest not only the importance of early intervention in epilepsy but the possible factor which correlates with disease severity. Moreover, by analyzing the propagation patterns of different seizures, we asserted the necessity of collecting sufficient data for identifying the epileptogenic networks.