Computing with a Canonical Neural Circuits Model with Pool Normalization and Modulating Feedback.
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| Title: | Computing with a Canonical Neural Circuits Model with Pool Normalization and Modulating Feedback. |
|---|---|
| Authors: | Brosch, Tobias, Neumann, Heiko |
| Source: | Neural Computation. 2014, Vol. 26 Issue 12, p2735-2789. 55p. 3 Diagrams, 3 Charts, 7 Graphs. |
| Subjects: | Neural circuitry, Feedback control systems, Information processing, Computer simulation, Prediction models, Neural computers |
| Abstract: | Evidence suggests that the brain uses an operational set of canonical computations like normalization, input filtering, and response gain enhancement via reentrant feedback. Here, we propose a three-stage columnar architecture of cascaded model neurons to describe a core circuit combining signal pathways of feedforward and feedback processing and the inhibitory pooling of neurons to normalize the activity. We present an analytical investigation of such a circuit by first reducing its detail through the lumping of initial feedforward response filtering and reentrant modulating signal amplification. The resulting excitatory-inhibitory pair of neurons is analyzed in a 2D phase-space. The inhibitory pool activation is treated as a separate mechanism exhibiting different effects. We analyze subtractive as well as divisive (shunting) interaction to implement center-surround mechanisms that include normalization effects in the characteristics of real neurons. Different variants of a core model architecture are derived and analyzed--in particular, individual excitatory neurons (without pool inhibition), the interactionwith an inhibitory subtractive or divisive (i.e., shunting) pool, and the dynamics of recurrent self-excitation combined with divisive inhibition. The stability and existence properties of these model instances are characterized, which serve as guidelines to adjust these properties through proper model parameterization. The significance of the derived results is demonstrated by theoretical predictions of response behaviors in the case of multiple interacting hypercolumns in a single and in multiple feature dimensions. In numerical simulations, we confirm these predictions and provide some explanations for different neural computational properties. A mongthose, we consider orientation contrast-dependent response behavior, different forms of attentional modulation, contrast element grouping, and the dynamic adaptation of the silent surround in extraclassical receptive field configurations, using only slight variations of the same core reference model. [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: 99342859 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Computing with a Canonical Neural Circuits Model with Pool Normalization and Modulating Feedback. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Brosch%2C+Tobias%22">Brosch, Tobias</searchLink><br /><searchLink fieldCode="AR" term="%22Neumann%2C+Heiko%22">Neumann, Heiko</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. 2014, Vol. 26 Issue 12, p2735-2789. 55p. 3 Diagrams, 3 Charts, 7 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Neural+circuitry%22">Neural circuitry</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+control+systems%22">Feedback control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Information+processing%22">Information processing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+computers%22">Neural computers</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Evidence suggests that the brain uses an operational set of canonical computations like normalization, input filtering, and response gain enhancement via reentrant feedback. Here, we propose a three-stage columnar architecture of cascaded model neurons to describe a core circuit combining signal pathways of feedforward and feedback processing and the inhibitory pooling of neurons to normalize the activity. We present an analytical investigation of such a circuit by first reducing its detail through the lumping of initial feedforward response filtering and reentrant modulating signal amplification. The resulting excitatory-inhibitory pair of neurons is analyzed in a 2D phase-space. The inhibitory pool activation is treated as a separate mechanism exhibiting different effects. We analyze subtractive as well as divisive (shunting) interaction to implement center-surround mechanisms that include normalization effects in the characteristics of real neurons. Different variants of a core model architecture are derived and analyzed--in particular, individual excitatory neurons (without pool inhibition), the interactionwith an inhibitory subtractive or divisive (i.e., shunting) pool, and the dynamics of recurrent self-excitation combined with divisive inhibition. The stability and existence properties of these model instances are characterized, which serve as guidelines to adjust these properties through proper model parameterization. The significance of the derived results is demonstrated by theoretical predictions of response behaviors in the case of multiple interacting hypercolumns in a single and in multiple feature dimensions. In numerical simulations, we confirm these predictions and provide some explanations for different neural computational properties. A mongthose, we consider orientation contrast-dependent response behavior, different forms of attentional modulation, contrast element grouping, and the dynamic adaptation of the silent surround in extraclassical receptive field configurations, using only slight variations of the same core reference model. [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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=99342859 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1162/NECO_a_00675 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 55 StartPage: 2735 Subjects: – SubjectFull: Neural circuitry Type: general – SubjectFull: Feedback control systems Type: general – SubjectFull: Information processing Type: general – SubjectFull: Computer simulation Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Neural computers Type: general Titles: – TitleFull: Computing with a Canonical Neural Circuits Model with Pool Normalization and Modulating Feedback. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Brosch, Tobias – PersonEntity: Name: NameFull: Neumann, Heiko IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 26 – Type: issue Value: 12 Titles: – TitleFull: Neural Computation Type: main |
| ResultId | 1 |
