Treffer: Astrocyte-Mediated Plasticity: Multi-Scale Mechanisms Linking Synaptic Dynamics to Learning and Memory.

Title:
Astrocyte-Mediated Plasticity: Multi-Scale Mechanisms Linking Synaptic Dynamics to Learning and Memory.
Source:
Cells (2073-4409); Dec2025, Vol. 14 Issue 24, p1936, 25p
Database:
Complementary Index

Weitere Informationen

Astrocytes play a pivotal role in shaping synaptic function and in learning, memory, and emotion. Recent studies show that perisynaptic astrocytic processes form structured interactions with pre- and postsynaptic elements, which extends synaptic diversity beyond neuron–neuron connections. Accumulating evidence indicates that astrocytic Ca<sup>2+</sup> signaling, gliotransmission, and local translation modulate synaptic efficacy and contribute to the formation and stabilization of memory traces. It is therefore essential to define how astrocytic microdomains, multisynaptic leaflet domains, and network-level ensembles cooperate to regulate circuit computation across space and time. Advances in super-resolution and volumetric in vivo imaging and spatial transcriptomics now enable detailed, cell-type- and compartment-specific analysis of astrocyte–synapse interactions in vivo. In this review, we highlight these approaches and synthesize classical and emerging mechanisms by which astrocytes read neuronal activity, write to synapses, and coordinate network states. We also discuss theoretical frameworks such as neuron–astrocyte associative memory models that formalize astrocytic calcium states as distributed substrates for storage and control. This integrated view provides new insight into the multicellular logic of memory and suggests paths toward understanding and treating neurological and psychiatric disorders. [ABSTRACT FROM AUTHOR]

Copyright of Cells (2073-4409) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)