Gamma Oscillations in aNonlinear Regime: A MinimalModel Approach Using Heterogeneous Integrate-and-Fire Networks.
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| Title: | Gamma Oscillations in aNonlinear Regime: A MinimalModel Approach Using Heterogeneous Integrate-and-Fire Networks. |
|---|---|
| Authors: | Bathellier, Brice, Carleton, Alan, Gerstner, Wulfram |
| Source: | Neural Computation. Dec2008, Vol. 20 Issue 12, p2973-3002. 30p. 2 Diagrams, 4 Graphs. |
| Subjects: | Biological neural networks, Nonlinear statistical models, Oscillating chemical reactions, Brain function localization, Neural circuitry, Neurosciences |
| Abstract: | Fast oscillations and in particular gamma-band oscillation (20-80 Hz) are commonly observed during brain function and are at the center of several neural processing theories. In many cases, mathematical analysis of fast oscillations in neural networks has been focused on the transition between irregular and oscillatory firing viewed as an instability of the asynchronous activity. But in fact, brain slice experiments as well as detailed simulations of biological neural networks have produced a large corpus of results concerning the properties of fully developed oscillations that are far from this transition point. We propose here a mathematical approach to deal with nonlinear oscillations in a network of heterogeneous or noisy integrate-and-fire neurons connected by strong inhibition. This approach involves limited mathematical complexity and gives a good sense of the oscillation mechanism, making it an interesting tool to understand fast rhythmic activity in simulated or biological neural networks. A surprising result of our approach is that under some conditions, a change of the strength of inhibition only weakly influences the period of the oscillation. This is in contrast to standard theoretical and experimental models of interneuron network gamma oscillations (ING), where frequency tightly depends on inhibition strength, but it is similar to observations made in some in vitro preparations in the hippocampus and the olfactory bulb and in some detailed network models. This result is explained by the phenomenon of suppression that is known to occur in strongly coupled oscillating inhibitory networks but had not yet been related to the behavior of oscillation frequency. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computation is the property of MIT Press 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: 35014230 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Gamma Oscillations in aNonlinear Regime: A MinimalModel Approach Using Heterogeneous Integrate-and-Fire Networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bathellier%2C+Brice%22">Bathellier, Brice</searchLink><br /><searchLink fieldCode="AR" term="%22Carleton%2C+Alan%22">Carleton, Alan</searchLink><br /><searchLink fieldCode="AR" term="%22Gerstner%2C+Wulfram%22">Gerstner, Wulfram</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Dec2008, Vol. 20 Issue 12, p2973-3002. 30p. 2 Diagrams, 4 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Biological+neural+networks%22">Biological neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+statistical+models%22">Nonlinear statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Oscillating+chemical+reactions%22">Oscillating chemical reactions</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+function+localization%22">Brain function localization</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+circuitry%22">Neural circuitry</searchLink><br /><searchLink fieldCode="DE" term="%22Neurosciences%22">Neurosciences</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Fast oscillations and in particular gamma-band oscillation (20-80 Hz) are commonly observed during brain function and are at the center of several neural processing theories. In many cases, mathematical analysis of fast oscillations in neural networks has been focused on the transition between irregular and oscillatory firing viewed as an instability of the asynchronous activity. But in fact, brain slice experiments as well as detailed simulations of biological neural networks have produced a large corpus of results concerning the properties of fully developed oscillations that are far from this transition point. We propose here a mathematical approach to deal with nonlinear oscillations in a network of heterogeneous or noisy integrate-and-fire neurons connected by strong inhibition. This approach involves limited mathematical complexity and gives a good sense of the oscillation mechanism, making it an interesting tool to understand fast rhythmic activity in simulated or biological neural networks. A surprising result of our approach is that under some conditions, a change of the strength of inhibition only weakly influences the period of the oscillation. This is in contrast to standard theoretical and experimental models of interneuron network gamma oscillations (ING), where frequency tightly depends on inhibition strength, but it is similar to observations made in some in vitro preparations in the hippocampus and the olfactory bulb and in some detailed network models. This result is explained by the phenomenon of suppression that is known to occur in strongly coupled oscillating inhibitory networks but had not yet been related to the behavior of oscillation frequency. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computation is the property of MIT Press 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1162/neco.2008.11-07-636 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 2973 Subjects: – SubjectFull: Biological neural networks Type: general – SubjectFull: Nonlinear statistical models Type: general – SubjectFull: Oscillating chemical reactions Type: general – SubjectFull: Brain function localization Type: general – SubjectFull: Neural circuitry Type: general – SubjectFull: Neurosciences Type: general Titles: – TitleFull: Gamma Oscillations in aNonlinear Regime: A MinimalModel Approach Using Heterogeneous Integrate-and-Fire Networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bathellier, Brice – PersonEntity: Name: NameFull: Carleton, Alan – PersonEntity: Name: NameFull: Gerstner, Wulfram IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2008 Type: published Y: 2008 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 20 – Type: issue Value: 12 Titles: – TitleFull: Neural Computation Type: main |
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