Postsynaptic burst reactivation of hippocampal neurons enables associative plasticity of temporally discontiguous inputs

  1. Tanja Fuchsberger
  2. Claudia Clopath
  3. Przemyslaw Jarzebowski
  4. Zuzanna Brzosko
  5. Hongbing Wang
  6. Ole Paulsen  Is a corresponding author
  1. University of Cambridge, United Kingdom
  2. Imperial College London, United Kingdom
  3. Michigan State University, United States

Abstract

A fundamental unresolved problem in neuroscience is how the brain associates in memory events that are separated in time. Here we propose that reactivation-induced synaptic plasticity can solve this problem. Previously, we reported that the reinforcement signal dopamine converts hippocampal spike timing-dependent depression into potentiation during continued synaptic activity (Brzosko et al., 2015). Here, we report that postsynaptic bursts in the presence of dopamine produce input-specific LTP in mouse hippocampal synapses 10 minutes after they were primed with coincident pre- and postsynaptic activity (post-before-pre pairing; Δt = -20 ms). This priming activity induces synaptic depression and sets an NMDA receptor-dependent silent eligibility trace which, through the cAMP-PKA cascade, is rapidly converted into protein synthesis-dependent synaptic potentiation, mediated by a signaling pathway distinct from that of conventional LTP. This synaptic learning rule was incorporated into a computational model, and we found that it adds specificity to reinforcement learning by controlling memory allocation and enabling both ‘instructive’ and 'supervised' reinforcement learning. We predicted that this mechanism would make reactivated neurons activate more strongly and carry more spatial information than non-reactivated cells, which was confirmed in freely moving mice performing a reward-based navigation task.

Data availability

Data availabilityExperimental data and code are available at:Code for computational model and code for in vivo analysis (including a link to in vivo data) are available at: https://github.com/przemyslawj/dCA1-reactivations. Data of plasticity experiments and of simulation data from computational model are available at: https://data.mendeley.com/datasets/dx7cdgpcz3/1.

The following data sets were generated

Article and author information

Author details

  1. Tanja Fuchsberger

    Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Claudia Clopath

    Department of Bioengineering, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4507-8648
  3. Przemyslaw Jarzebowski

    Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Zuzanna Brzosko

    Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Hongbing Wang

    Department of Physiology, Michigan State University, East Lansing, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Ole Paulsen

    Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
    For correspondence
    op210@cam.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2258-5455

Funding

Biotechnology and Biological Sciences Research Council (BB/N019008/1)

  • Tanja Fuchsberger
  • Zuzanna Brzosko
  • Ole Paulsen

Biotechnology and Biological Sciences Research Council (BB/P019560/1)

  • Tanja Fuchsberger
  • Claudia Clopath
  • Ole Paulsen

Biotechnology and Biological Sciences Research Council (Studentship)

  • Przemyslaw Jarzebowski

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

Ethics

Animal experimentation: Experimental procedures and animal use were performed in accordance with UK Home Office regulations of the UK Animals (Scientific Procedures) Act 1986 and Amendment Regulations 2012, following ethical review by the University of Cambridge Animal Welfare and Ethical Review Body (AWERB). All animal procedures were authorized under Personal and Project licences held by the authors.

Copyright

© 2022, Fuchsberger 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

  • 2,461
    views
  • 393
    downloads
  • 13
    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. Tanja Fuchsberger
  2. Claudia Clopath
  3. Przemyslaw Jarzebowski
  4. Zuzanna Brzosko
  5. Hongbing Wang
  6. Ole Paulsen
(2022)
Postsynaptic burst reactivation of hippocampal neurons enables associative plasticity of temporally discontiguous inputs
eLife 11:e81071.
https://doi.org/10.7554/eLife.81071

Share this article

https://doi.org/10.7554/eLife.81071

Further reading

    1. Neuroscience
    Moritz F Wurm, Doruk Yiğit Erigüç
    Research Article

    Recognizing goal-directed actions is a computationally challenging task, requiring not only the visual analysis of body movements, but also analysis of how these movements causally impact, and thereby induce a change in, those objects targeted by an action. We tested the hypothesis that the analysis of body movements and the effects they induce relies on distinct neural representations in superior and anterior inferior parietal lobe (SPL and aIPL). In four fMRI sessions, participants observed videos of actions (e.g. breaking stick, squashing plastic bottle) along with corresponding point-light-display (PLD) stick figures, pantomimes, and abstract animations of agent–object interactions (e.g. dividing or compressing a circle). Cross-decoding between actions and animations revealed that aIPL encodes abstract representations of action effect structures independent of motion and object identity. By contrast, cross-decoding between actions and PLDs revealed that SPL is disproportionally tuned to body movements independent of visible interactions with objects. Lateral occipitotemporal cortex (LOTC) was sensitive to both action effects and body movements. These results demonstrate that parietal cortex and LOTC are tuned to physical action features, such as how body parts move in space relative to each other and how body parts interact with objects to induce a change (e.g. in position or shape/configuration). The high level of abstraction revealed by cross-decoding suggests a general neural code supporting mechanical reasoning about how entities interact with, and have effects on, each other.

    1. Neuroscience
    Magdalena Solyga, Georg B Keller
    Research Article

    Our movements result in predictable sensory feedback that is often multimodal. Based on deviations between predictions and actual sensory input, primary sensory areas of cortex have been shown to compute sensorimotor prediction errors. How prediction errors in one sensory modality influence the computation of prediction errors in another modality is still unclear. To investigate multimodal prediction errors in mouse auditory cortex, we used a virtual environment to experimentally couple running to both self-generated auditory and visual feedback. Using two-photon microscopy, we first characterized responses of layer 2/3 (L2/3) neurons to sounds, visual stimuli, and running onsets and found responses to all three stimuli. Probing responses evoked by audiomotor (AM) mismatches, we found that they closely resemble visuomotor (VM) mismatch responses in visual cortex (V1). Finally, testing for cross modal influence on AM mismatch responses by coupling both sound amplitude and visual flow speed to the speed of running, we found that AM mismatch responses were amplified when paired with concurrent VM mismatches. Our results demonstrate that multimodal and non-hierarchical interactions shape prediction error responses in cortical L2/3.