Associative synaptic plasticity creates dynamic persistent activity.

Authors

Albert J Wakhloo, David G Clark, L F Abbott

Published in

Proceedings of the National Academy of Sciences of the United States of America. Volume 123. Issue 40. Pages e2526156123. Oct 06, 2026. Epub Sep 29, 2026.

Abstract

In biological neural circuits, the dynamics of neurons and synapses are tightly coupled. We study the consequences of this coupling and show that it enables a form of working memory. In recurrent neural network models with ongoing Hebbian plasticity, we find that following oscillatory stimulation, neurons continue to oscillate long after the input is removed. This creates a dynamic form of memory that has no explicit storage or retrieval phases and that requires no prior knowledge of the input. We trace the mechanism of these "persistent oscillations" to an interaction between neurons and synapses that creates complex outlier eigenvalues of the connectivity matrix. This is shown both in simulation and analytically. We leverage this mechanistic understanding to generate persistent oscillations with prespecified dynamics, creating a dynamic analog of a classical Hopfield network. Our work demonstrates that coupling neuronal and synaptic dynamics enables novel forms of computation.

PMID:
42809398
Bibliographic data and abstract were imported from PubMed on 30 Sep 2026.

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