Synchronization Patterns in Neural Chains based on Hyper-Chaotic Cells
Abstract
Synchronization patterns in chains of N bi-directionally delayed coupled systems with delayed feedback are studied in this paper. Each system is hyper-chaotic when decoupled from the chain. It is shown that chains with odd or even number of cites N display different spatial patterns of stable exact synchronization. When N is odd the only stable pattern of exact synchronization is among all of the units. When N is even, next to the nearest neighbors could become exactly synchronized, with the dynamics of the nearest neighbors related in a more complicated way. Sufficiently strong coupling leads to the nearest neighbor synchronization also for even N. No other patterns have been observed.
Keywords:
Time lag / synchronization / hyper-chaotic system / Ikeda model / Mackey-Glass modelSource:
9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings, 2008, 51-Publisher:
- IEEE, New York
Funding / projects:
- Ministry of Science - 1443
Collections
Institution/Community
PharmacyTY - CONF AU - Burić, Nikola AU - Todorović, Kristina AU - Vasović, Nebojša AU - Samcović, Andreja PY - 2008 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1044 AB - Synchronization patterns in chains of N bi-directionally delayed coupled systems with delayed feedback are studied in this paper. Each system is hyper-chaotic when decoupled from the chain. It is shown that chains with odd or even number of cites N display different spatial patterns of stable exact synchronization. When N is odd the only stable pattern of exact synchronization is among all of the units. When N is even, next to the nearest neighbors could become exactly synchronized, with the dynamics of the nearest neighbors related in a more complicated way. Sufficiently strong coupling leads to the nearest neighbor synchronization also for even N. No other patterns have been observed. PB - IEEE, New York C3 - 9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings T1 - Synchronization Patterns in Neural Chains based on Hyper-Chaotic Cells SP - 51 DO - 10.1109/NEUREL.2008.4685559 ER -
@conference{ author = "Burić, Nikola and Todorović, Kristina and Vasović, Nebojša and Samcović, Andreja", year = "2008", abstract = "Synchronization patterns in chains of N bi-directionally delayed coupled systems with delayed feedback are studied in this paper. Each system is hyper-chaotic when decoupled from the chain. It is shown that chains with odd or even number of cites N display different spatial patterns of stable exact synchronization. When N is odd the only stable pattern of exact synchronization is among all of the units. When N is even, next to the nearest neighbors could become exactly synchronized, with the dynamics of the nearest neighbors related in a more complicated way. Sufficiently strong coupling leads to the nearest neighbor synchronization also for even N. No other patterns have been observed.", publisher = "IEEE, New York", journal = "9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings", title = "Synchronization Patterns in Neural Chains based on Hyper-Chaotic Cells", pages = "51", doi = "10.1109/NEUREL.2008.4685559" }
Burić, N., Todorović, K., Vasović, N.,& Samcović, A.. (2008). Synchronization Patterns in Neural Chains based on Hyper-Chaotic Cells. in 9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings IEEE, New York., 51. https://doi.org/10.1109/NEUREL.2008.4685559
Burić N, Todorović K, Vasović N, Samcović A. Synchronization Patterns in Neural Chains based on Hyper-Chaotic Cells. in 9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings. 2008;:51. doi:10.1109/NEUREL.2008.4685559 .
Burić, Nikola, Todorović, Kristina, Vasović, Nebojša, Samcović, Andreja, "Synchronization Patterns in Neural Chains based on Hyper-Chaotic Cells" in 9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings (2008):51, https://doi.org/10.1109/NEUREL.2008.4685559 . .