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

Area of Science:

• Neuroscience
• Computational Neuroscience
• Synaptic Plasticity

Background:

• Pyramidal cells (PCs) are crucial for neocortical structure and function.
• Synaptic plasticity in PCs is fundamental to brain learning mechanisms.
• Limited experimental data on plasticity across diverse PC types hinders understanding of neocortical learning.

Purpose of the Study:

• To develop a data-constrained model of synaptic plasticity.
• To unify experimental findings on long-term potentiation (LTP) and long-term depression (LTD) in neocortical PCs.
• To investigate the drivers of diverse plasticity outcomes across different PC types.

Main Methods:

• Developed a computational model of postsynaptic calcium dynamics.
• Simulated synaptic plasticity in a neocortical microcircuit model.
• Analyzed plasticity outcomes under varying physiological conditions (in vitro vs. in vivo).

Main Results:

• A single parameter set successfully explained diverse LTP/LTD findings across PC types.
• Cell-type-specific synaptic physiology, morphology, and innervation patterns account for varied plasticity.
• Predicted distinct plasticity dynamics in vivo compared to in vitro conditions.

Conclusions:

• The model provides a comprehensive null model for neocortical PC plasticity.
• Cell-specific intrinsic properties, not distinct plasticity rules, explain diverse synaptic plasticity outcomes.
• The framework supports further development of cortical synaptic plasticity models.