Associative synaptic plasticity creates dynamic persistent activity
4 affiliations
- 1Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY, 10027
- 2Center for Computational Neuroscience, Flatiron Institute, New York, NY, 10010
- 3Kavli Institute for Brain Science, Columbia University, New York, NY, 10027
- 4Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Boston, MA, 02134
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.
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BibTeX
@article{wakhloo2026associative,
title = {{Associative synaptic plasticity creates dynamic persistent activity}},
author = {Albert J. Wakhloo and David G. Clark and L.F. Abbott},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = sep,
doi = {10.1073/pnas.2526156123},
url = {https://doi.org/10.1073/pnas.2526156123},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Associative synaptic plasticity creates dynamic persistent activity
AU - Albert J. Wakhloo
AU - David G. Clark
AU - L.F. Abbott
PY - 2026
DA - 2026/09/29
JO - Proceedings of the National Academy of Sciences of the United States of America
DO - 10.1073/pnas.2526156123
UR - https://doi.org/10.1073/pnas.2526156123
AB - 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.
SN - 0027-8424
SN - 1091-6490
ER -