1 Ocak 2016 Dergi makalesi Açık Erişim

Fen, Mehmet Onur; Akhmet, Marat

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/55623</identifier>
  <creators>
    <creator>
      <creatorName>Fen, Mehmet Onur</creatorName>
      <givenName>Mehmet Onur</givenName>
      <familyName>Fen</familyName>
      <affiliation>Middle E Tech Univ, Dept Math, TR-06800 Ankara, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Akhmet, Marat</creatorName>
      <givenName>Marat</givenName>
      <familyName>Akhmet</familyName>
      <affiliation>Middle E Tech Univ, Dept Math, TR-06800 Ankara, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Impulsive Sicnns With Chaotic Postsynaptic Currents</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2016</publicationYear>
  <dates>
    <date dateType="Issued">2016-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/55623</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.3934/dcdsb.2016.21.1119</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">In the present study, we investigate the dynamics of shunting inhibitory cellular neural networks (SICNNs) with impulsive effects. We give a mathematical description of the chaos for the multidimensional dynamics of impulsive SICNNs, and prove its existence rigorously by taking advantage of the external inputs. The Li-Yorke definition of chaos is used in our theoretical discussions. In the considered model, the impacts satisfy the cell and shunting principles. This enriches the applications of SICNNs and makes the analysis of impulsive neural networks deeper. The technique is exceptionally useful for SICNNs with arbitrary number of cells. We make benefit of unidirectionally coupled SICNNs to exemplify our results. Moreover, the appearance of cyclic irregular behavior observed in neuroscience is numerically demonstrated for discontinuous dynamics of impulsive SICNNs.</description>
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