NYU Dataset

Synaptic Homeostasis Transiently Leverages Hebbian Mechanisms for a Multiphasic Response to Inactivity

Part of: Tsien Lab |
UID: 10756
* Corresponding Author
Description

Synaptic plasticity is assumed to be a key biological substrate of learning, memory, and neural circuit development. Two forms of plasticity are thought to enable neurons to integrate recent activity changes without veering into extreme hypoactivity or hyperactivity. This includes Hebbian positive feedback to strengthen active synapses, long-term potentiation(LTP), and diminish less active ones, long-term depression (LTD), and homeostatic negative feedback to maintain functional stability. It is widely believed that synaptic scaling is a slow first-order process that regulates postsynaptic glutamate receptors and fundamentally differs from LTP or LTD. This study found that the dynamics of scaling induced by neuronal inactivity are not exponential or monotonic, and the mechanism requires calcineurin and CaMKII, molecules dominant in LTD and LTP. Using experimental and computational approaches, they described how synapses use Hebbian and homeostatic elements to adapt to changing activity levels while retaining the capacity for plasticity. The dataset contains electrophysiology and imaging data.

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Access

Restrictions
Free to All
Instructions
All experimental data and analysis code has been deposited on Open Science Framework (OSF) and modeling code has been deposited on GitHub.
Access via OSF

Experimental data and analysis code

Access via GitHub

Modeling code

Associated Publications
Data Type
Equipment Used
Zeiss LSM 800
Software Used
GraphPad Prism
Icy
MATLAB
pCLAMP v10.0
Grant Support