Spiking Neuron Model for Temporal Sequence Recognition.
Saved in:
| Title: | Spiking Neuron Model for Temporal Sequence Recognition. |
|---|---|
| Authors: | Byrnes, Sean, Burkitt, Anthony N., Grayden, David B., Meffin, Hamish |
| Source: | Neural Computation. Jan2010, Vol. 22 Issue 1, p61-93. 33p. 2 Diagrams, 1 Chart, 7 Graphs. |
| Subjects: | Biological neural networks, Recursive partitioning, Neuroplasticity, Retroactive interference (Psychology), Perturbation theory, Neural circuitry |
| Abstract: | A biologically inspired neuronal network that stores and recognizes temporal sequences of symbols is described. Each symbol is represented by excitatory input to distinct groups of neurons (symbol pools). Unambiguous storage of multiple sequences with common subsequences is ensured by partitioning each symbol pool into subpools that respond only when the current symbol has been preceded by a particular sequence of symbols. We describe synaptic structure and neural dynamics that permit the selective activation of subpools by the correct sequence. Symbols may have varying durations of the order of hundreds of milliseconds. Physiologically plausible plasticity mechanisms operate on a time scale of tens of milliseconds; an interaction of the excitatory input with periodic global inhibition bridges this gap so that neural events representing successive symbols occur on this much faster timescale. The network is shown to store multiple overlapping sequences of events. It is robust to variation in symbol duration, it is scalable, and its performance degrades gracefully with perturbation of its parameters. [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 |
|---|---|
| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 46816429 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Spiking Neuron Model for Temporal Sequence Recognition. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Byrnes%2C+Sean%22">Byrnes, Sean</searchLink><br /><searchLink fieldCode="AR" term="%22Burkitt%2C+Anthony+N%2E%22">Burkitt, Anthony N.</searchLink><br /><searchLink fieldCode="AR" term="%22Grayden%2C+David+B%2E%22">Grayden, David B.</searchLink><br /><searchLink fieldCode="AR" term="%22Meffin%2C+Hamish%22">Meffin, Hamish</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Jan2010, Vol. 22 Issue 1, p61-93. 33p. 2 Diagrams, 1 Chart, 7 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="%22Recursive+partitioning%22">Recursive partitioning</searchLink><br /><searchLink fieldCode="DE" term="%22Neuroplasticity%22">Neuroplasticity</searchLink><br /><searchLink fieldCode="DE" term="%22Retroactive+interference+%28Psychology%29%22">Retroactive interference (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Perturbation+theory%22">Perturbation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+circuitry%22">Neural circuitry</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A biologically inspired neuronal network that stores and recognizes temporal sequences of symbols is described. Each symbol is represented by excitatory input to distinct groups of neurons (symbol pools). Unambiguous storage of multiple sequences with common subsequences is ensured by partitioning each symbol pool into subpools that respond only when the current symbol has been preceded by a particular sequence of symbols. We describe synaptic structure and neural dynamics that permit the selective activation of subpools by the correct sequence. Symbols may have varying durations of the order of hundreds of milliseconds. Physiologically plausible plasticity mechanisms operate on a time scale of tens of milliseconds; an interaction of the excitatory input with periodic global inhibition bridges this gap so that neural events representing successive symbols occur on this much faster timescale. The network is shown to store multiple overlapping sequences of events. It is robust to variation in symbol duration, it is scalable, and its performance degrades gracefully with perturbation of its parameters. [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=46816429 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1162/neco.2009.12-07-679 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 33 StartPage: 61 Subjects: – SubjectFull: Biological neural networks Type: general – SubjectFull: Recursive partitioning Type: general – SubjectFull: Neuroplasticity Type: general – SubjectFull: Retroactive interference (Psychology) Type: general – SubjectFull: Perturbation theory Type: general – SubjectFull: Neural circuitry Type: general Titles: – TitleFull: Spiking Neuron Model for Temporal Sequence Recognition. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Byrnes, Sean – PersonEntity: Name: NameFull: Burkitt, Anthony N. – PersonEntity: Name: NameFull: Grayden, David B. – PersonEntity: Name: NameFull: Meffin, Hamish IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2010 Type: published Y: 2010 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 22 – Type: issue Value: 1 Titles: – TitleFull: Neural Computation Type: main |
| ResultId | 1 |
