Slow dynamics and high variability in balanced cortical networks with clustered connections.
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| Title: | Slow dynamics and high variability in balanced cortical networks with clustered connections. |
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| Authors: | Litwin-Kumar, Ashok, Doiron, Brent |
| Source: | Nature Neuroscience. Nov2012, Vol. 15 Issue 11, p1498-1505. 8p. 7 Diagrams, 1 Graph. |
| Subjects: | Neurons, Excitation (Physiology), Fluctuations (Physics), Poisson processes, Response inhibition |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Nature Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 82862777 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Slow dynamics and high variability in balanced cortical networks with clustered connections. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Litwin-Kumar%2C+Ashok%22">Litwin-Kumar, Ashok</searchLink><br /><searchLink fieldCode="AR" term="%22Doiron%2C+Brent%22">Doiron, Brent</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Nature+Neuroscience%22">Nature Neuroscience</searchLink>. Nov2012, Vol. 15 Issue 11, p1498-1505. 8p. 7 Diagrams, 1 Graph. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Neurons%22">Neurons</searchLink><br /><searchLink fieldCode="DE" term="%22Excitation+%28Physiology%29%22">Excitation (Physiology)</searchLink><br /><searchLink fieldCode="DE" term="%22Fluctuations+%28Physics%29%22">Fluctuations (Physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Poisson+processes%22">Poisson processes</searchLink><br /><searchLink fieldCode="DE" term="%22Response+inhibition%22">Response inhibition</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Nature Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=82862777 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1038/nn.3220 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 1498 Subjects: – SubjectFull: Neurons Type: general – SubjectFull: Excitation (Physiology) Type: general – SubjectFull: Fluctuations (Physics) Type: general – SubjectFull: Poisson processes Type: general – SubjectFull: Response inhibition Type: general Titles: – TitleFull: Slow dynamics and high variability in balanced cortical networks with clustered connections. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Litwin-Kumar, Ashok – PersonEntity: Name: NameFull: Doiron, Brent IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 10976256 Numbering: – Type: volume Value: 15 – Type: issue Value: 11 Titles: – TitleFull: Nature Neuroscience Type: main |
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