Frequency Selectivity Emerging from Spike-Timing-Dependent Plasticity.
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| Title: | Frequency Selectivity Emerging from Spike-Timing-Dependent Plasticity. |
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| Authors: | Gilson, Matthieu, Bürck, Moritz, Burkitt, Anthony N., van Hemmen, J. Leo |
| Source: | Neural Computation. Sep2012, Vol. 24 Issue 9, p2251-2279. 29p. 1 Diagram, 1 Chart, 7 Graphs. |
| Subjects: | Neuroplasticity, Synapses, Dendritic cells, Mathematical analysis, Learning, Physiological adaptation |
| Abstract: | Periodic neuronal activity has been observed in various areas of the brain, from lower sensory to higher cortical levels. Specific frequency components contained in this periodic activity can be identified by a neuronal circuit that behaves as a bandpass filterwith given preferred frequency, or best modulation frequency (BMF). For BMFs typically ranging from 10 to 200 Hz, a plausible and minimal configuration consists of a single neuron with adjusted excitatory and inhibitory synaptic connections. The emergence, however, of such a neuronal circuitry is still unclear. In this letter, we demonstrate how spike-timing-dependent plasticity (STDP) can give rise to frequency-dependent learning, thus leading to an input selectivity that enables frequency identification. We use an in-depth mathematical analysis of the learning dynamics in a population of plastic inhibitory connections. These provide inhomogeneous postsynaptic responses that depend on their dendritic location. We find that synaptic delays play a crucial role in organizing the weight specialization induced by STDP. Under suitable conditions on the synaptic delays and postsynaptic potentials (PSPs), the BMF of a neuron after learning can match the training frequency. In particular, proximal (distal) synapses with shorter (longer) dendritic delay and somatically measured PSP time constants respond better to higher (lower) frequencies. As a result, the neuron will respond maximally to any stimulating frequency (in a given range) with which it has been trained in an unsupervised manner. The model predicts that synapses responding to a given BMF form clusters on dendritic branches. [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: 78349355 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Frequency Selectivity Emerging from Spike-Timing-Dependent Plasticity. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gilson%2C+Matthieu%22">Gilson, Matthieu</searchLink><br /><searchLink fieldCode="AR" term="%22Bürck%2C+Moritz%22">Bürck, Moritz</searchLink><br /><searchLink fieldCode="AR" term="%22Burkitt%2C+Anthony+N%2E%22">Burkitt, Anthony N.</searchLink><br /><searchLink fieldCode="AR" term="%22van+Hemmen%2C+J%2E+Leo%22">van Hemmen, J. Leo</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Sep2012, Vol. 24 Issue 9, p2251-2279. 29p. 1 Diagram, 1 Chart, 7 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Neuroplasticity%22">Neuroplasticity</searchLink><br /><searchLink fieldCode="DE" term="%22Synapses%22">Synapses</searchLink><br /><searchLink fieldCode="DE" term="%22Dendritic+cells%22">Dendritic cells</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+analysis%22">Mathematical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Learning%22">Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Physiological+adaptation%22">Physiological adaptation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Periodic neuronal activity has been observed in various areas of the brain, from lower sensory to higher cortical levels. Specific frequency components contained in this periodic activity can be identified by a neuronal circuit that behaves as a bandpass filterwith given preferred frequency, or best modulation frequency (BMF). For BMFs typically ranging from 10 to 200 Hz, a plausible and minimal configuration consists of a single neuron with adjusted excitatory and inhibitory synaptic connections. The emergence, however, of such a neuronal circuitry is still unclear. In this letter, we demonstrate how spike-timing-dependent plasticity (STDP) can give rise to frequency-dependent learning, thus leading to an input selectivity that enables frequency identification. We use an in-depth mathematical analysis of the learning dynamics in a population of plastic inhibitory connections. These provide inhomogeneous postsynaptic responses that depend on their dendritic location. We find that synaptic delays play a crucial role in organizing the weight specialization induced by STDP. Under suitable conditions on the synaptic delays and postsynaptic potentials (PSPs), the BMF of a neuron after learning can match the training frequency. In particular, proximal (distal) synapses with shorter (longer) dendritic delay and somatically measured PSP time constants respond better to higher (lower) frequencies. As a result, the neuron will respond maximally to any stimulating frequency (in a given range) with which it has been trained in an unsupervised manner. The model predicts that synapses responding to a given BMF form clusters on dendritic branches. [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_a_00331 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 2251 Subjects: – SubjectFull: Neuroplasticity Type: general – SubjectFull: Synapses Type: general – SubjectFull: Dendritic cells Type: general – SubjectFull: Mathematical analysis Type: general – SubjectFull: Learning Type: general – SubjectFull: Physiological adaptation Type: general Titles: – TitleFull: Frequency Selectivity Emerging from Spike-Timing-Dependent Plasticity. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gilson, Matthieu – PersonEntity: Name: NameFull: Bürck, Moritz – PersonEntity: Name: NameFull: Burkitt, Anthony N. – PersonEntity: Name: NameFull: van Hemmen, J. Leo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 24 – Type: issue Value: 9 Titles: – TitleFull: Neural Computation Type: main |
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