Academic Journal
Understanding the Emergence of Modularity in Neural Systems.
| Τίτλος: | Understanding the Emergence of Modularity in Neural Systems. |
|---|---|
| Συγγραφείς: | Bullinaria, John A. |
| Πηγή: | Cognitive Science; Jul2007, Vol. 31 Issue 4, p673-695, 23p, 1 Diagram, 8 Graphs |
| Θεματικοί όροι: | Emergence (Philosophy), Modularity (Psychology), Human information processing, Cognition, Computational linguistics, Algorithms, Learning, Nervous system, Evolutionary theories |
| Περίληψη: | Modularity in the human brain remains a controversial issue, with disagreement over the nature of the modules that exist, and why, when, and how they emerge. It is a natural assumption that modularity offers some form of computational advantage, and hence evolution by natural selection has translated those advantages into the kind of modular neural structures familiar to cognitive scientists. However, simulations of the evolution of simplified neural systems have shown that, in many cases, it is actually non-modular architectures that are most efficient. In this paper, the relevant issues are discussed and a series of simulations are presented that reveal crucial dependencies on the details of the learning algorithms and tasks that are being modelled, and the importance of taking into account known physical brain constraints, such as the degree of neural connectivity. A pattern is established which provides one explanation of why modularity should emerge reliably across a range of neural processing tasks. [ABSTRACT FROM AUTHOR] |
| Copyright of Cognitive Science is the property of Wiley-Blackwell 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://www.doi.org/10.1080/15326900701399939? Name: ScienceDirect (all content) (s7799221) Category: fullText Text: View record from ScienceDirect MouseOverText: View record from ScienceDirect |
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| Items | – Name: Title Label: Title Group: Ti Data: Understanding the Emergence of Modularity in Neural Systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bullinaria%2C+John+A%2E%22">Bullinaria, John A.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Cognitive Science; Jul2007, Vol. 31 Issue 4, p673-695, 23p, 1 Diagram, 8 Graphs – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Emergence+%28Philosophy%29%22">Emergence (Philosophy)</searchLink><br /><searchLink fieldCode="DE" term="%22Modularity+%28Psychology%29%22">Modularity (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Human+information+processing%22">Human information processing</searchLink><br /><searchLink fieldCode="DE" term="%22Cognition%22">Cognition</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+linguistics%22">Computational linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Learning%22">Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Nervous+system%22">Nervous system</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+theories%22">Evolutionary theories</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Modularity in the human brain remains a controversial issue, with disagreement over the nature of the modules that exist, and why, when, and how they emerge. It is a natural assumption that modularity offers some form of computational advantage, and hence evolution by natural selection has translated those advantages into the kind of modular neural structures familiar to cognitive scientists. However, simulations of the evolution of simplified neural systems have shown that, in many cases, it is actually non-modular architectures that are most efficient. In this paper, the relevant issues are discussed and a series of simulations are presented that reveal crucial dependencies on the details of the learning algorithms and tasks that are being modelled, and the importance of taking into account known physical brain constraints, such as the degree of neural connectivity. A pattern is established which provides one explanation of why modularity should emerge reliably across a range of neural processing tasks. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Cognitive Science is the property of Wiley-Blackwell 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: 10.1080/15326900701399939 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 673 Subjects: – SubjectFull: Emergence (Philosophy) Type: general – SubjectFull: Modularity (Psychology) Type: general – SubjectFull: Human information processing Type: general – SubjectFull: Cognition Type: general – SubjectFull: Computational linguistics Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Learning Type: general – SubjectFull: Nervous system Type: general – SubjectFull: Evolutionary theories Type: general Titles: – TitleFull: Understanding the Emergence of Modularity in Neural Systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bullinaria, John A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 03640213 Numbering: – Type: volume Value: 31 – Type: issue Value: 4 Titles: – TitleFull: Cognitive Science Type: main |
| ResultId | 1 |