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Modeling of epilepsy based on chaotic artificial neural network

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  • Panahi, Shirin
  • Aram, Zainab
  • Jafari, Sajad
  • Ma, Jun
  • Sprott, J.C.

Abstract

Epilepsy is a long-term chronic neurological disorder that is characterized by seizures. One type of epilepsy is simple partial seizures that are localized to one area on one side of the brain, especially in the temporal lobe, but some may spread from there. GABA (gamma-aminobutyric acid) is an inhibitory neurotransmitter that is widely distributed in the neurons of the cortex. Scientists recently discovered the basic role of neurotransmitters in epilepsy. Synaptic reorganizations at GABAergic and glutamatergic synapses not only enable seizure occurrence, they also modify the normal information processing performed by these networks. Based on some physiological facts about epilepsy and chaos, a behavioral model is presented in this paper. This model represents the problem of undesired seizure, and also tries to suggest different valuable predictions about possible causes of epilepsy disorder. The proposed model suggests that there is a possible interaction between the role of excitatory and inhibitory neurotransmitters and epilepsy. The result of these studies might be helpful to discern epilepsy in a different way and give some guidance to predict the occurrence of seizures in patients.

Suggested Citation

  • Panahi, Shirin & Aram, Zainab & Jafari, Sajad & Ma, Jun & Sprott, J.C., 2017. "Modeling of epilepsy based on chaotic artificial neural network," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 150-156.
    • Handle: RePEc:eee:chsofr:v:105:y:2017:i:c:p:150-156
    DOI: 10.1016/j.chaos.2017.10.028
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    1. Erkaymaz, Okan & Ozer, Mahmut & Perc, Matjaž, 2017. "Performance of small-world feedforward neural networks for the diagnosis of diabetes," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 22-28.
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