Mechanisms of Homeostatic Synaptic Plasticity in vivo

Research output: Contribution to journalReview article

Abstract

Synapses undergo rapid activity-dependent plasticity to store information, which when left uncompensated can lead to destabilization of neural function. It has been well documented that homeostatic changes, which operate at a slower time scale, are required to maintain stability of neural networks. While there are many mechanisms that can endow homeostatic control, sliding threshold and synaptic scaling are unique in that they operate by providing homeostatic control of synaptic strength. The former mechanism operates by adjusting the threshold for synaptic plasticity, while the latter mechanism directly alters the gain of synapses. Both modes of homeostatic synaptic plasticity have been studied across various preparations from reduced in vitro systems, such as neuronal cultures, to in vivo intact circuitry. While most of the cellular and molecular mechanisms of homeostatic synaptic plasticity have been worked out using reduced preparations, there are unique challenges present in intact circuitry in vivo, which deserve further consideration. For example, in an intact circuit, neurons receive distinct set of inputs across their dendritic tree which carry unique information. Homeostatic synaptic plasticity in vivo needs to operate without compromising processing of these distinct set of inputs to preserve information processing while maintaining network stability. In this mini review, we will summarize unique features of in vivo homeostatic synaptic plasticity, and discuss how sliding threshold and synaptic scaling may act across different activity regimes to provide homeostasis.

Original languageEnglish (US)
Article number520
JournalFrontiers in Cellular Neuroscience
Volume13
DOIs
StatePublished - Dec 3 2019

Keywords

  • BCM theory
  • cortical plasticity
  • hebbian plasticity
  • homeostasis
  • metaplasticity
  • sliding threshold
  • synaptic scaling

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience

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