Stable task information from an unstable neural population
- University of Cambridge, United Kingdom
- Stanford University, United States
- 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.
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Data from: Stable task information from an unstable neural populationDryad Digital Repository, 10.5061/dryad.gqnk98sjq.
Article and author information
Author details
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.
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.
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Stable task information from an unstable neural population
eLife 9:e51121.
https://doi.org/10.7554/eLife.51121
