1. Neuroscience

A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity

  1. TD Barbara Nguyen-Vu  Is a corresponding author
  2. Grace Q Zhao
  3. Subhaneil Lahiri
  4. Rhea R Kimpo
  5. Hanmi Lee
  6. Surya Ganguli
  7. Carla J Shatz
  8. Jennifer L Raymond  Is a corresponding author
  1. Stanford School of Medicine, United States
https://doi.org/10.7554/eLife.20147
Share this article
Cite this article
  1. TD Barbara Nguyen-Vu
  2. Grace Q Zhao
  3. Subhaneil Lahiri
  4. Rhea R Kimpo
  5. Hanmi Lee
  6. Surya Ganguli
  7. Carla J Shatz
  8. Jennifer L Raymond
(2017)
A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity
eLife 6:e20147.
https://doi.org/10.7554/eLife.20147
  2. Download BibTeX
  3. Download .RIS

Abstract

Across many studies, animals with enhanced synaptic plasticity exhibit either enhanced or impaired learning, raising a conceptual puzzle: how enhanced plasticity can yield opposite learning outcomes? Here we show that recent history of experience can determine whether mice with enhanced plasticity exhibit enhanced or impaired learning in response to the same training. Mice with enhanced cerebellar LTD, due to double knockout (DKO) of MHCI H2-Kb/H2-Db (KbDb-/-), exhibited oculomotor learning deficits. However, the same mice exhibited enhanced learning after appropriate pre-training. Theoretical analysis revealed that synapses with history-dependent learning rules could recapitulate the data, and suggested that saturation may be a key factor limiting the ability of enhanced plasticity to enhance learning. Moreover, optogenetic stimulation designed to saturate LTD produced the same impairment in WT as observed in DKO mice. Overall, our results suggest that recent history of activity and the threshold for synaptic plasticity conspire to effect divergent learning outcomes.

Article and author information

Author details

  1. TD Barbara Nguyen-Vu

    Department of Neurobiology, Stanford School of Medicine, Stanford, United States
    For correspondence
    ngbabs@gmail.com
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4708-1982

  2. Grace Q Zhao

    Department of Neurobiology, Stanford School of Medicine, Stanford, United States
    Competing interests
    No competing interests declared.

  3. Subhaneil Lahiri

    Department of Neurobiology, Stanford School of Medicine, Stanford, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2028-6635

  4. Rhea R Kimpo

    Department of Neurobiology, Stanford School of Medicine, Stanford, United States
    Competing interests
    No competing interests declared.

  5. Hanmi Lee

    Department of Neurobiology, Stanford School of Medicine, Stanford, United States
    Competing interests
    No competing interests declared.

  6. Surya Ganguli

    Department of Neurobiology, Stanford School of Medicine, Stanford, United States
    Competing interests
    No competing interests declared.

  7. Carla J Shatz

    Department of Neurobiology, Stanford School of Medicine, Stanford, United States
    Competing interests
    No competing interests declared.

  8. Jennifer L Raymond

    Department of Neurobiology, Stanford School of Medicine, Stanford, United States
    For correspondence
    jenr@stanford.edu
    Competing interests
    Jennifer L Raymond, Reviewing editor, eLife.

Funding

National Institutes of Health (RO1DC04154,RO1NS072406,R21NS057488,P30DC10363)

  • Jennifer L Raymond

National Science Foundation (Graduate Research Fellowship)

  • TD Barbara Nguyen-Vu

Burroughs Wellcome Fund

  • Surya Ganguli

Genentech Foundation

  • Hanmi Lee

James S. McDonnell Foundation

  • Jennifer L Raymond

National Institutes of Health (F31DC010547)

  • TD Barbara Nguyen-Vu

National Institutes of Health (F32NS058060)

  • Grace Q Zhao

National Institutes of Health (RO1MH07166)

  • Carla J Shatz

National Institutes of Health (NS069375)

  • Jennifer L Raymond

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

Reviewing Editor

  1. Mark CW van Rossum, University of Edinburgh, United Kingdom

Ethics

Animal experimentation: All experimental procedures were approved by the Administrative Panel on Laboratory Animal Care at Stanford University under animal care and use committee (IACUC) Protocol #9143, titled 'Vestibular and Visual Control of Eye Movements in Mice'.

Version history

  1. Received: July 29, 2016
  2. Accepted: February 2, 2017
  3. Accepted Manuscript published: February 24, 2017 (version 1)
  4. Version of Record published: April 10, 2017 (version 2)

Copyright

© 2017, Nguyen-Vu 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

  • 3,152
    views
  • 608
    downloads
  • 14
    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. TD Barbara Nguyen-Vu
  2. Grace Q Zhao
  3. Subhaneil Lahiri
  4. Rhea R Kimpo
  5. Hanmi Lee
  6. Surya Ganguli
  7. Carla J Shatz
  8. Jennifer L Raymond
(2017)
A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity
eLife 6:e20147.
https://doi.org/10.7554/eLife.20147

Share this article

https://doi.org/10.7554/eLife.20147

Categories and tags

Research organism

Further reading

    1. Neuroscience

    A stochastic world model on gravity for stability inference

    Taicheng Huang, Jia Liu
    Research Article

    The fact that objects without proper support will fall to the ground is not only a natural phenomenon, but also common sense in mind. Previous studies suggest that humans may infer objects’ stability through a world model that performs mental simulations with a priori knowledge of gravity acting upon the objects. Here we measured participants’ sensitivity to gravity to investigate how the world model works. We found that the world model on gravity was not a faithful replica of the physical laws, but instead encoded gravity’s vertical direction as a Gaussian distribution. The world model with this stochastic feature fit nicely with participants’ subjective sense of objects’ stability and explained the illusion that taller objects are perceived as more likely to fall. Furthermore, a computational model with reinforcement learning revealed that the stochastic characteristic likely originated from experience-dependent comparisons between predictions formed by internal simulations and the realities observed in the external world, which illustrated the ecological advantage of stochastic representation in balancing accuracy and speed for efficient stability inference. The stochastic world model on gravity provides an example of how a priori knowledge of the physical world is implemented in mind that helps humans operate flexibly in open-ended environments.

    1. Neuroscience

    Drift of neural ensembles driven by slow fluctuations of intrinsic excitability

    Geoffroy Delamare, Yosif Zaki ... Claudia Clopath
    Short Report

    Representational drift refers to the dynamic nature of neural representations in the brain despite the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The model predicts that drift is induced by co-activation of previously active neurons along with neurons with high excitability which leads to remodeling of the recurrent weights. Consistent with previous experimental works, the drifting ensemble is informative about its temporal history. Crucially, we show that the gradual nature of the drift is necessary for decoding temporal information from the activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an output neuron having plastic synapses with the main region.

    1. Cell Biology
    2. Neuroscience

    Synapsin E-domain is essential for α-synuclein function

    Alexandra Stavsky, Leonardo A Parra-Rivas ... Daniel Gitler
    Short Report

    The cytosolic proteins synucleins and synapsins are thought to play cooperative roles in regulating synaptic vesicle (SV) recycling, but mechanistic insight is lacking. Here, we identify the synapsin E-domain as an essential functional binding-partner of α-synuclein (α-syn). Synapsin E-domain allows α-syn functionality, binds to α-syn, and is necessary and sufficient for enabling effects of α-syn at synapses of cultured mouse hippocampal neurons. Together with previous studies implicating the E-domain in clustering SVs, our experiments advocate a cooperative role for these two proteins in maintaining physiologic SV clusters.