Toward Unified Hybrid Simulation Techniques for Spiking Neural Networks.
Saved in:
| Title: | Toward Unified Hybrid Simulation Techniques for Spiking Neural Networks. |
|---|---|
| Authors: | D'Haene, Michiel, Hermans, Michiel, Schrauwen, Benjamin |
| Source: | Neural Computation. Jun2014, Vol. 26 Issue 6, p1055-1079. 25p. 1 Graph. |
| Subjects: | Hybrid computer simulation, Biological neural networks, Event driven systems (Computer science), Perceptrons, Computational neuroscience |
| Abstract: | In the field of neural network simulation techniques, the common conception is that spiking neural network simulators can be divided in two categories: time-step-based and event-driven methods. In this letter, we look at state-of-the art simulation techniques in both categories and show that a clear distinction between both methods is increasingly difficult to define. In an attempt to improve the weak points of each simulation method, ideas of the alternativemethod are, sometimes unknowingly, incorporated in the simulation engine. Clearly the ideal simulation method is a mix of both methods. We formulate the key properties of such an efficient and generally applicable hybrid approach. [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: 95950051 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Toward Unified Hybrid Simulation Techniques for Spiking Neural Networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22D'Haene%2C+Michiel%22">D'Haene, Michiel</searchLink><br /><searchLink fieldCode="AR" term="%22Hermans%2C+Michiel%22">Hermans, Michiel</searchLink><br /><searchLink fieldCode="AR" term="%22Schrauwen%2C+Benjamin%22">Schrauwen, Benjamin</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Jun2014, Vol. 26 Issue 6, p1055-1079. 25p. 1 Graph. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hybrid+computer+simulation%22">Hybrid computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+neural+networks%22">Biological neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Event+driven+systems+%28Computer+science%29%22">Event driven systems (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Perceptrons%22">Perceptrons</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+neuroscience%22">Computational neuroscience</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the field of neural network simulation techniques, the common conception is that spiking neural network simulators can be divided in two categories: time-step-based and event-driven methods. In this letter, we look at state-of-the art simulation techniques in both categories and show that a clear distinction between both methods is increasingly difficult to define. In an attempt to improve the weak points of each simulation method, ideas of the alternativemethod are, sometimes unknowingly, incorporated in the simulation engine. Clearly the ideal simulation method is a mix of both methods. We formulate the key properties of such an efficient and generally applicable hybrid approach. [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=95950051 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1162/NECO_a_00587 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 1055 Subjects: – SubjectFull: Hybrid computer simulation Type: general – SubjectFull: Biological neural networks Type: general – SubjectFull: Event driven systems (Computer science) Type: general – SubjectFull: Perceptrons Type: general – SubjectFull: Computational neuroscience Type: general Titles: – TitleFull: Toward Unified Hybrid Simulation Techniques for Spiking Neural Networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: D'Haene, Michiel – PersonEntity: Name: NameFull: Hermans, Michiel – PersonEntity: Name: NameFull: Schrauwen, Benjamin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 26 – Type: issue Value: 6 Titles: – TitleFull: Neural Computation Type: main |
| ResultId | 1 |
