Statistical Approaches for the Analysis of Dependency Among Neurons Under Noise
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...
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| Format: | Article |
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MDPI AG
2020-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/4/387 |
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| author | Deniz Gençağa Sevgi Şengül Ayan Hajar Farnoudkia Serdar Okuyucu |
| author_facet | Deniz Gençağa Sevgi Şengül Ayan Hajar Farnoudkia Serdar Okuyucu |
| author_sort | Deniz Gençağa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T10:11:03Z |
| format | Article |
| id | doaj.art-9850f17d304c474096e7f91a00718a81 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T10:11:03Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-9850f17d304c474096e7f91a00718a812023-11-16T14:33:48ZengMDPI AGEntropy1099-43002020-03-0122438710.3390/e22040387Statistical Approaches for the Analysis of Dependency Among Neurons Under NoiseDeniz Gençağa0Sevgi Şengül Ayan1Hajar Farnoudkia2Serdar Okuyucu3Department of Electrical and Electronics Engineering, Antalya Bilim University, 07190 Antalya, TurkeyDepartment of Industrial Engineering, Antalya Bilim University, 07190 Antalya, TurkeyDepartment of Statistics, Middle East Technical University, 06800 Ankara, TurkeyDepartment of Electrical and Electronics Engineering, Antalya Bilim University, 07190 Antalya, TurkeyNeuronal 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.https://www.mdpi.com/1099-4300/22/4/387transfer entropymutual informationinformation theorycopulasHodgkin–Huxley model |
| spellingShingle | Deniz Gençağa Sevgi Şengül Ayan Hajar Farnoudkia Serdar Okuyucu Statistical Approaches for the Analysis of Dependency Among Neurons Under Noise Entropy transfer entropy mutual information information theory copulas Hodgkin–Huxley model |
| title | Statistical Approaches for the Analysis of Dependency Among Neurons Under Noise |
| title_full | Statistical Approaches for the Analysis of Dependency Among Neurons Under Noise |
| title_fullStr | Statistical Approaches for the Analysis of Dependency Among Neurons Under Noise |
| title_full_unstemmed | Statistical Approaches for the Analysis of Dependency Among Neurons Under Noise |
| title_short | Statistical Approaches for the Analysis of Dependency Among Neurons Under Noise |
| title_sort | statistical approaches for the analysis of dependency among neurons under noise |
| topic | transfer entropy mutual information information theory copulas Hodgkin–Huxley model |
| url | https://www.mdpi.com/1099-4300/22/4/387 |
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