By Quansheng Ren, Qiufeng Long, Zhiqiang Zhang, Jianye Zhao (auth.), Chengan Guo, Zeng-Guang Hou, Zhigang Zeng (eds.)
The two-volume set LNCS 7951 and 7952 constitutes the refereed lawsuits of the tenth overseas Symposium on Neural Networks, ISNN 2013, held in Dalian, China, in July 2013. The 157 revised complete papers awarded have been rigorously reviewed and chosen from quite a few submissions. The papers are equipped in following issues: computational neuroscience, cognitive technology, neural community types, studying algorithms, balance and convergence research, kernel equipment, huge margin tools and SVM, optimization algorithms, varational equipment, regulate, robotics, bioinformatics and biomedical engineering, brain-like structures and brain-computer interfaces, facts mining and information discovery and different functions of neural networks.
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Additional resources for Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part I
Virtual neurons are very similar to the real ones and show typical decay function that conforms to biological detail. We concluded that the developmental scales λ1 and λ2 have little effect on this distribution. Sholl-like plots of the bifurcations or terminations versus branch order or path distance yielded classical bell shaped curves for all real and virtual neurons. In the same way, the distributions of the number of branches versus branch order are shown in Fig. 5(c), (d). Here, the number of branches is equal to number of bifurcations plus number of terminations.
Each edge is directed from a gene that encodes a transcription factor to a gene that is regulated by that transcription factor. A genetic system is defined as a network of N interacting nodes. The genetic regulatory network is structured in three layers, consisting NI input nodes, NR regulatory nodes, and NO output nodes respectively. NI and NO are determined by the developmental model described in the below section. The regulatory nodes that recurrently connect in the network represent genes that play a regulatory role only.
Primal and Dual Neural Networks for Shortest-Path Routing, Systems, Man and Cybernetics. com/lncs Abstract. In the olfactory system, both the temporal spike structure and spatial distribution of neuronal activity are important for processing odor information. In this paper, a biophysically-detailed, spiking neuronal model is used to simulate the activity of olfactory bulb. It is shown that by varying some key parameters such as maximal conductances of Ks, N ap and N a, the spike train of single neuron can exhibit various ﬁring patterns.
Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part I by Quansheng Ren, Qiufeng Long, Zhiqiang Zhang, Jianye Zhao (auth.), Chengan Guo, Zeng-Guang Hou, Zhigang Zeng (eds.)