A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex

Nat Commun. 2022 Jun 1;13(1):3038. doi: 10.1038/s41467-022-30214-w.


Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie learning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections. In particular, we find that the diverse plasticity outcomes across the different PC types can be explained by cell-type-specific synaptic physiology, cell morphology and innervation patterns, without requiring type-specific plasticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict qualitatively different plasticity dynamics from those observed in vitro. This work provides a first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open framework for further developing models of cortical synaptic plasticity.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Calcium / metabolism
  • Depression
  • Long-Term Potentiation* / physiology
  • Neocortex*
  • Neuronal Plasticity / physiology


  • Calcium