Endocannabinoid Mediated Long-Term Depression at Inhibitory Synapses



Long-lasting activity-dependent changes in synaptic strength, in the form of long-term potentiation (LTP) or depression (LTD), are thought to be the cellular basis of learning and memory. Although inhibitory synapses are critical for proper functioning of neural circuits, most of the studies investigating synaptic plasticity have concentrated on excitatory glutamatergic synapses. The idea that inhibitory synapses are plastic like excitatory synapses is gradually being accepted. Different forms of GABAergic synaptic plasticity have recently been reported. Among them, one of the most intensely studied is a form of LTD mediated by a group of retrograde messengers, collectively called endocannabinoids (eCBs). eCB-mediated LTD at inhibitory synapses (I-LTD) is a heterosynaptic form of plasticity, whose induction typically requires the activation of metabotropic glutamatergic receptors by nearby excitatory inputs and the resulting eCB mobilization from the postsynaptic cell. By activating presynaptic type 1 cannabinoid receptors (CB1Rs), eCBs can cause a long-lasting reduction in GABA release. I-LTD has been identified in several brain structures and may serve as a mechanism by which neurons adjust the strength of the inhibition they receive in response to excitatory afferent stimulation. In light of the wide-ranging effects of inhibition on synaptic transmission, from shaping the input-output relationship and excitability of neurons to modulating the inducibility of excitatory synaptic plasticity, I-LTD is expected to have significant impact on the excitatory/inhibitory balance within circuits and on neural network function. In this chapter, we review the main properties of eCB-mediated LTD at GABAergic synapses with the aim of understanding its physiological role.


Gaba Release Excitatory Synapse Inhibitory Synapse Theta Burst Stimulation Postsynaptic Cell 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Supported by NIH/NIDA and by NARSAD. We thank Thomas Younts for critical reading of the manuscript.


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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Dominick P. Purpura Department of NeuroscienceAlbert Einstein College of MedicineBronxUSA

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