Synaptic scaling in combination with many generic plasticity mechanisms stabilizes circuit connectivity

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Synaptic scaling is a slow process regulating neural circuit firing rates.
• Conventional synaptic plasticity (LTD/LTP) also modifies synaptic patterns.
• The generation and stabilization of synaptic patterns remain poorly understood.

Purpose of the Study:

• To formally describe and analyze synaptic scaling.
• To demonstrate how synaptic scaling and plasticity regulate network connectivity.
• To model synaptic distributions and modifications during activity changes.

Main Methods:

• Analysis of experimental results on synaptic scaling.
• Development of computational models simulating synaptic plasticity and scaling.
• Modeling of synaptic distributions and activity-dependent modifications.

Main Results:

• The combination of synaptic plasticity and scaling provides a framework for regulating network connectivity.
• Models reproduce experimentally observed synaptic distributions and modifications.
• The combined mechanism generates globally stable, input-controlled synaptic patterns, even in recurrent networks.

Conclusions:

• Synaptic scaling and plasticity robustly yield neuronal circuits with high synaptic diversity.
• This diversity enables robust dynamic storage of complex activation patterns.
• The mechanism is enhanced by inhibition and may underlie learning in initially random networks.