Modified Bifurcating Neuron with Leaky-Integrate-and-Fire Model

Simple item page
dc.contributor.authorRisinger, Lon
dc.contributor.authorKaikhah, Khosrow
dc.date.accessioned2012-02-24T10:17:51Z
dc.date.available2012-02-24T10:17:51Z
dc.date.issued2004-05
dc.description.abstractThe Modified Bifurcating Neuron (MBN) is a neuron model that is capable of amplitude-to-phase conversion and volume-holographic memory. Inputs are real valued and temporally spaced. This allows information to be coded in the temporal spacing of inputs and outputs as well as their values. At its core, the MBN incorporates a stateful leaky-integrate-and-fire neuron model. The MBN attempts to produce these properties by simulating mechanisms present in biological neural systems to a greater extent than is normally found in artificial neural networks. MBNs use an object model rather than the normal linear algebra approach. The MBN is conceptually based on the computational model presented in the "Bifurcating Neuron Network 2" by G. Lee and N. Farhat.
dc.description.departmentComputer Science
dc.formatText
dc.format.extent10 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationRisinger, L. & Kaikhah, K. (2004). Modified bifurcating neuron with leaky-integrate-and-fire model. <i>Proceedings of the 17th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems</i>, pp. 1033-1042.
dc.identifier.doihttps://doi.org/10.1007/978-3-540-24677-0_106
dc.identifier.urihttps://hdl.handle.net/10877/3814
dc.language.isoen
dc.publisherSpringer, Berlin, Heidelberg
dc.sourceProceedings of the Seventeenth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 2004, Ottawa, Canada
dc.subjectbifurcating
dc.subjectneuron
dc.subjectleaky-integrate-and-fire model
dc.subjectMBN
dc.subjectneural networks
dc.titleModified Bifurcating Neuron with Leaky-Integrate-and-Fire Model
dc.typeArticle

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
fulltext.pdf
Size:
109.68 KB
Format:
Adobe Portable Document Format
Download

Collections

College of Science and Engineering