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We develop a completely data-driven approach to reconstructing coupled neuronal networks that contain a small subset of chaotic neurons. Such chaotic elements can be the result of parameter shift in

We develop a completely data-driven approach to reconstructing coupled neuronal networks that contain a small subset of chaotic neurons. Such chaotic elements can be the result of parameter shift in their individual dynamical systems and may lead to abnormal functions of the network. To accurately identify the chaotic neurons may thus be necessary and important, for example, applying appropriate controls to bring the network to a normal state.

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    Date Created
    • 2014-07-01
    Resource Type
  • Text
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    Identifier
    • Digital object identifier: 10.3390/e16073889
    • Identifier Type
      International standard serial number
      Identifier Value
      1099-4300

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    Su, Ri-Qi, Lai, Ying-Cheng, & Wang, Xiao (2014). Identifying Chaotic FitzHugh-Nagumo Neurons Using Compressive Sensing. ENTROPY, 16(7), 3889-3902. http://dx.doi.org/10.3390/e16073889

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