Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Associative synaptic plasticity creates dynamic persistent activity. |
| Συγγραφείς: |
Wakhloo AJ; Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027.; Center for Computational Neuroscience, Flatiron Institute, New York, NY 10010., Clark DG; Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027.; Kavli Institute for Brain Science, Columbia University, New York, NY 10027.; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Boston, MA 02134., Abbott LF; Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY 10027.; Kavli Institute for Brain Science, Columbia University, New York, NY 10027. |
| Πηγή: |
Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2026 Oct 06; Vol. 123 (40), pp. e2526156123. Date of Electronic Publication: 2026 Sep 29. |
| Τύπος έκδοσης: |
Journal Article |
| Γλώσσα: |
English |
| Στοιχεία περιοδικού: |
Publisher: National Academy of Sciences Country of Publication: United States NLM ID: 7505876 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1091-6490 (Electronic) Linking ISSN: 00278424 NLM ISO Abbreviation: Proc Natl Acad Sci U S A Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: Washington, DC : National Academy of Sciences |
| Ιατρικοί όροι (MeSH): |
Neuronal Plasticity*/physiology , Neurons*/physiology , Synapses*/physiology , Models, Neurological*, Nerve Net/physiology ; Memory, Short-Term/physiology ; Animals ; Computer Simulation |
| Περίληψη: |
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. |
| Competing Interests: |
Competing interests statement:The authors declare no competing interest. |
| Σχόλια: |
Update of: bioRxiv. 2025 Aug 24:2025.08.20.671131. doi: 10.1101/2025.08.20.671131.. (PMID: 40894763) |
| Grant Information: |
N/A Gatsby Charitable Foundation (GATSBY); N/A Kavli Foundation (Kavli); T32NS064929 HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS); T32MH126036 HHS | NIH | National Institute of Mental Health (NIMH) |
| Contributed Indexing: |
Keywords: neural networks; nonlinear dynamics; random matrix theory; synaptic plasticity; working memory |
| Entry Date(s): |
Date Created: 20260929 Date Completed: 20260929 Latest Revision: 20261006 |
| Update Code: |
20261006 |
| DOI: |
10.1073/pnas.2526156123 |
| PMID: |
42809398 |
| Βάση Δεδομένων: |
MEDLINE |