Energy efficiency and coding of neural network
Based on the Hodgkin-Huxley model, this study explored the energy efficiency of BA network, ER network, WS network, and Caenorhabditis elegans neural network, and explained the development of neural network structure in the brain from the perspective of energy efficiency using energy coding theory....
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-01-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.1089373/full |
| _version_ | 1797955961219973120 |
|---|---|
| author | Shengnan Li Chuankui Yan Ying Liu |
| author_facet | Shengnan Li Chuankui Yan Ying Liu |
| author_sort | Shengnan Li |
| collection | DOAJ |
| description | Based on the Hodgkin-Huxley model, this study explored the energy efficiency of BA network, ER network, WS network, and Caenorhabditis elegans neural network, and explained the development of neural network structure in the brain from the perspective of energy efficiency using energy coding theory. The numerical simulation results showed that the BA network had higher energy efficiency, which was closer to that of the C. elegans neural network, indicating that the neural network in the brain had scale-free property because of satisfying high energy efficiency. In addition, the relationship between the energy consumption of neural networks and synchronization was established by applying energy coding. The stronger the neural network synchronization was, the less energy the network consumed. |
| first_indexed | 2024-04-10T23:41:20Z |
| format | Article |
| id | doaj.art-4a1c5da62fc64a91a5ef2d0e8ff4698f |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-10T23:41:20Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-4a1c5da62fc64a91a5ef2d0e8ff4698f2023-01-11T07:01:51ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-01-011610.3389/fnins.2022.10893731089373Energy efficiency and coding of neural networkShengnan LiChuankui YanYing LiuBased on the Hodgkin-Huxley model, this study explored the energy efficiency of BA network, ER network, WS network, and Caenorhabditis elegans neural network, and explained the development of neural network structure in the brain from the perspective of energy efficiency using energy coding theory. The numerical simulation results showed that the BA network had higher energy efficiency, which was closer to that of the C. elegans neural network, indicating that the neural network in the brain had scale-free property because of satisfying high energy efficiency. In addition, the relationship between the energy consumption of neural networks and synchronization was established by applying energy coding. The stronger the neural network synchronization was, the less energy the network consumed.https://www.frontiersin.org/articles/10.3389/fnins.2022.1089373/fullHodgkin-Huxley neuronal modelneural networkenergy efficiencyenergy codinginformation entropy |
| spellingShingle | Shengnan Li Chuankui Yan Ying Liu Energy efficiency and coding of neural network Frontiers in Neuroscience Hodgkin-Huxley neuronal model neural network energy efficiency energy coding information entropy |
| title | Energy efficiency and coding of neural network |
| title_full | Energy efficiency and coding of neural network |
| title_fullStr | Energy efficiency and coding of neural network |
| title_full_unstemmed | Energy efficiency and coding of neural network |
| title_short | Energy efficiency and coding of neural network |
| title_sort | energy efficiency and coding of neural network |
| topic | Hodgkin-Huxley neuronal model neural network energy efficiency energy coding information entropy |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.1089373/full |
| work_keys_str_mv | AT shengnanli energyefficiencyandcodingofneuralnetwork AT chuankuiyan energyefficiencyandcodingofneuralnetwork AT yingliu energyefficiencyandcodingofneuralnetwork |