The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Slow dynamics and high variability in balanced cortical networks with clustered connections
Open accessAbstract
Anatomical studies demonstrate that excitatory connections in cortex are not uniformly distributed across a network but instead exhibit clustering into groups of highly connected neurons. The implications of clustering for cortical activity are unclear. We studied the effect of clustered excitatory connections on the dynamics of neuronal networks that exhibited high spike time variability owing to a balance between excitation and inhibition. Even modest clustering substantially changed the behavior of these networks, introducing slow dynamics during which clusters of neurons transiently increased or decreased their firing rate. Consequently, neurons exhibited both fast spiking variability and slow firing rate fluctuations. A simplified model shows how stimuli bias networks toward particular activity states, thereby reducing firing rate variability as observed experimentally in many cortical areas. Our model thus relates cortical architecture to the reported variability in spontaneous and evoked spiking activity.
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He, B. J. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.04.003 · Cited by 802
The Role of Variability in Motor Learning
Dhawale, A. K., Smith, M. A., & Ölveczky, B. P. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031548 · Cited by 516
Robust timing and motor patterns by taming chaos in recurrent neural networks
Laje, R., & Buonomano, D. V. (2013) · Nature Neuroscience · doi:10.1038/nn.3405 · Cited by 454
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Metadata
- Authors
- Litwin-Kumar, A., & Doiron, B.
- Venue
- Nature Neuroscience
- Published
- 2012-09-23
- Volume
- 15
- Issue
- 11
- Pages
- 1498-1505
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2137761603
- Cited by
- 539
- References
- 17
- FWCI
- 18.88
- Type
- journal article
- Language
- en
- Open access
- green
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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.
