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 court cases of the tenth overseas Symposium on Neural Networks, ISNN 2013, held in Dalian, China, in July 2013. The 157 revised complete papers provided have been conscientiously reviewed and chosen from quite a few submissions. The papers are geared up 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 tools, keep an eye on, robotics, bioinformatics and biomedical engineering, brain-like platforms and brain-computer interfaces, info mining and information discovery and different purposes of neural networks.
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Additional info for Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part I
1. The algorithm generates each dendritic tree as an independent process. For generation of the motoneurons, one of the basic variables is Nstem, which is the number of stems. Once a value Nstem is sampled for this variable, the algorithm is repeated Nstem times to generate the appropriate number of stems. Each stem originates from the soma with a certain initial diameter Dstem and an orientation specified by Saz and Selev. Then in the spherical coordinate system, the dendrite is defined by the spherical coordinates of its end point taking the starting point as the origin (Fig.
Trends in Neuroscience 21(11), 460–468 (1998) 2. : The Blue Brain Project. Nature Reviews Neuroscience 7(2), 153–160 (2006) 3. : Dendritic morphology, local circuitry, and intrinsic electrophysiology of principal neurons in the entorhinal cortex of macaque monkeys. Journal of Comparative Neurology 470(3), 317–329 (2004) 4. : The role of single neurons in information processing. Nature Neuroscience 3(1), 1171–1177 (2000) 5. : Neuronal shape parameters and substructures as a basis of neuronal form.
C2n , . . , cn1 , . . , cn,n−1 )T ∈ Rn(n−1) , A is an n(n − 1) × n matrix of 0, 1 and −1 to construct n(n − 1) inequality constraints, g[0,1] (v) = (g[0,1] (v1 ), . . , g[0,1] (vn−1 )), and its components are deﬁned as ⎧ ⎪ if v > 0, ⎨1, g[0,1] (vi ) = [0, 1], (6) if v = 0, (i = 1, 2, . . , n − 1) ⎪ ⎩ 0, if v < 0. 4. It consists of three main parts. The preprocessing part converts a certain network structure to a linear programming in the form of (4). Then the neural network processing part calculates the optimal solution.