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dc.contributor.authorGuo, Shangjiang ( Orcid Icon 0000-0002-9114-5269 )
dc.contributor.authorHuang, Lihong ( )
dc.contributor.authorWu, Jianhong ( )
dc.date.accessioned2020-11-25T16:03:36Z
dc.date.available2020-11-25T16:03:36Z
dc.date.issued2003-05-26
dc.identifier.citationGuo, S., Huang, L., & Wu, J. (2003). Convergence and periodicity in a delayed network of neurons with threshold nonlinearity. Electronic Journal of Differential Equations, 2003(61), pp. 1-14.en_US
dc.identifier.issn1072-6691
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/13001
dc.description.abstractWe consider an artificial neural network where the signal transmission is of a digital (McCulloch-Pitts) nature and is delayed due to the finite switching speed of neurons (amplifiers). The discontinuity of the signal transmission functions, however, makes it difficult to apply the existing dynamical systems theory which usually requires continuity and smoothness. Moreover, observe that the dynamics of the network completely depends on the connection weights, we distinguish several cases to discuss the behaviors of their solutions. We show that the dynamics of the model can be understood in terms of the iterations of a one-dimensional map. As, a result, we present a detailed analysis of the dynamics of the network starting from non-oscillatory states and show how the connection topology and synaptic weights determine the rich dynamics.en_US
dc.formatText
dc.format.extent14 pages
dc.format.medium1 file (.pdf)
dc.language.isoenen_US
dc.publisherSouthwest Texas State University, Department of Mathematicsen_US
dc.sourceElectronic Journal of Differential Equations, 2003, San Marcos, Texas: Southwest Texas State University and University of North Texas.
dc.subjectNeural networksen_US
dc.subjectFeedbacken_US
dc.subjectMcCulloch-Pitts nonlinearityen_US
dc.subjectOne-dimensional mapen_US
dc.subjectConvergenceen_US
dc.subjectPeriodic solutionen_US
dc.titleConvergence and periodicity in a delayed network of neurons with threshold nonlinearityen_US
dc.typepublishedVersion
txstate.documenttypeArticle
dc.rights.licenseCreative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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