A stochastic model of hippocampal synaptic plasticity with geometrical readout of enzyme dynamics

Yuri Elias Rodrigues, Cezar M Tigaret, Hélène Marie, Cian O'Donnell, Romain Veltz, Mark CW van Rossum (Editor), John R Huguenard

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
30 Downloads (Pure)

Abstract

Discovering the rules of synaptic plasticity is an important step for understanding brain learning. Existing plasticity models are either (1) top-down and interpretable, but not flexible enough to account for experimental data, or (2) bottom-up and biologically realistic, but too intricate to interpret and hard to fit to data. To avoid the shortcomings of these approaches, we present a new plasticity rule based on a geometrical readout mechanism that flexibly maps synaptic enzyme dynamics to predict plasticity outcomes. We apply this readout to a multi-timescale model of hippocampal synaptic plasticity induction that includes electrical dynamics, calcium, CaMKII and calcineurin, and accurate representation of intrinsic noise sources. Using a single set of model parameters, we demonstrate the robustness of this plasticity rule by reproducing nine published ex vivo experiments covering various spike-timing and frequency-dependent plasticity induction protocols, animal ages, and experimental conditions. Our model also predicts that in vivo-like spike timing irregularity strongly shapes plasticity outcome. This geometrical readout modelling approach can be readily applied to other excitatory or inhibitory synapses to discover their synaptic plasticity rules.
Original languageEnglish
Article numbere80152
Pages (from-to)1-63
Number of pages63
JournaleLife
Volume12
Early online date17 Aug 2023
DOIs
Publication statusPublished online - 17 Aug 2023

Bibliographical note

Publisher Copyright:
© 2023, eLife Sciences Publications Ltd. All rights reserved.

Keywords

  • synaptic plasticity
  • Other
  • hippocampus
  • computational neurosciences
  • computational biology
  • systems biology
  • neuroscience

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