Identification of biological neural network using jumping gene genetic algorithm
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | IECON Proceedings (Industrial Electronics Conference) |
| Pages | 693-697 |
| Publication status | Published - 2007 |
Conference
| Title | 33rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2007) |
|---|---|
| Location | Grand Hotel |
| Place | Taiwan |
| City | Taipei |
| Period | 5 - 8 November 2007 |
Link(s)
Abstract
In this paper, a jumping gene genetic algorithm is adopted to identify the topology of biological neural networks. The neural network is modeled with Hindmarsh-Rose neurons with synaptic coupling. Based on a single observable state of each neuron, it is possible to reveal the topology of the entire network under a framework of synchronization. The simulation results demonstrate that the topological structure of a neural network can be estimated accurately even if the exact models of the neurons are unknown. ©2007 IEEE.
Citation Format(s)
Identification of biological neural network using jumping gene genetic algorithm. / Yin, J. J.; Tang, Wallace; Man, K. F.
IECON Proceedings (Industrial Electronics Conference). 2007. p. 693-697 4460066.Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
