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Cited by 300

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Background gamma rhythmicity and attention in cortical local circuits: A computational study

Open access
Börgers, Christoph, Epstein, Steven, Kopell, Nancy J.·2005·Proceedings of the National Academy of Sciences

Abstract

We describe a simple computational model, based on generic features of cortical local circuits, that links cholinergic neuromodulation, gamma rhythmicity, and attentional selection. We propose that cholinergic modulation, by reducing adaptation currents in principal cells, induces a transition from asynchronous spontaneous activity to a “background” gamma rhythm (resembling the persistent gamma rhythms evoked in vitro by cholinergic agonists) in which individual principal cells participate infrequently and irregularly. We suggest that such rhythms accompany states of preparatory attention or vigilance and report simulations demonstrating that their presence can amplify stimulus-specific responses and enhance stimulus competition within a local circuit.

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Crossref · 10.1073/pnas.0502366102

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Metadata

Authors
Börgers, C., Epstein, S., & Kopell, N. J.
Venue
Proceedings of the National Academy of Sciences
Published
2005-05-03
Volume
102
Issue
19
Pages
7002-7007
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2133125918
Cited by
300
References
9
FWCI
6.54
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Background gamma rhythmicity and attention in cortical local circuits: A computational study. https://app.cochat.ai/papers/10.1073/pnas.0502366102
The paper
Börgers, C., Epstein, S., & Kopell, N. J. (2005). Background gamma rhythmicity and attention in cortical local circuits: A computational study. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0502366102

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.