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Open AccessArticle

Statistical Approaches for the Analysis of Dependency Among Neurons Under Noise

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Department of Electrical and Electronics Engineering, Antalya Bilim University, 07190 Antalya, Turkey
2
Department of Industrial Engineering, Antalya Bilim University, 07190 Antalya, Turkey
3
Department of Statistics, Middle East Technical University, 06800 Ankara, Turkey
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(4), 387; https://doi.org/10.3390/e22040387
Received: 6 February 2020 / Revised: 9 March 2020 / Accepted: 23 March 2020 / Published: 28 March 2020
Neuronal noise is a major factor affecting the communication between coupled neurons. In this work, we propose a statistical toolset to infer the coupling between two neurons under noise. We estimate these statistical dependencies from data which are generated by a coupled Hodgkin–Huxley (HH) model with additive noise. To infer the coupling using observation data, we employ copulas and information-theoretic quantities, such as the mutual information (MI) and the transfer entropy (TE). Copulas and MI between two variables are symmetric quantities, whereas TE is asymmetric. We demonstrate the performances of copulas and MI as functions of different noise levels and show that they are effective in the identification of the interactions due to coupling and noise. Moreover, we analyze the inference of TE values between neurons as a function of noise and conclude that TE is an effective tool for finding out the direction of coupling between neurons under the effects of noise. View Full-Text
Keywords: transfer entropy; mutual information; information theory; copulas; Hodgkin–Huxley model transfer entropy; mutual information; information theory; copulas; Hodgkin–Huxley model
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Gençağa, D.; Şengül Ayan, S.; Farnoudkia, H.; Okuyucu, S. Statistical Approaches for the Analysis of Dependency Among Neurons Under Noise. Entropy 2020, 22, 387.

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