Get Advances in Neural Networks – ISNN 2013: 10th International PDF

By Quansheng Ren, Qiufeng Long, Zhiqiang Zhang, Jianye Zhao (auth.), Chengan Guo, Zeng-Guang Hou, Zhigang Zeng (eds.)

ISBN-10: 3642390641

ISBN-13: 9783642390647

ISBN-10: 364239065X

ISBN-13: 9783642390654

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 offered have been conscientiously reviewed and chosen from a variety of submissions. The papers are equipped in following themes: computational neuroscience, cognitive technology, neural community types, studying algorithms, balance and convergence research, kernel equipment, huge margin tools and SVM, optimization algorithms, varational equipment, keep an eye on, robotics, bioinformatics and biomedical engineering, brain-like structures and brain-computer interfaces, facts mining and information discovery and different purposes of neural networks.

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Extra info for Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part I

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For other layers (i = 2, . . , k), the ith layer has (n− i + 1) intermediate nodes. It can be proved that the upper bound for the number of nodes is nk. Then link the nodes in adjacent layers and assign the weights properly, a network representing all k-subsets of n elements can be constructed. Therefore, it is clear that finding the L smallest k-subsets sum is equivalent to searching L shortest paths in a network. 3 Fig. 2. A network representing all 2-subsets of {2, 4, 6, 9} 3 Finding L Shortest Paths in a Network There are several algorithms available for finding L shortest paths in a network, among which the algorithm proposed by Yen is one of the most efficient in term of the number of operations and the number of memory addresses[12].

Xn ]T ∈ Rn , l is an integer greater than one and k is a nonnegative integer less than n. Compared with the optimization formulation of kWTA problem[11], conditions (C1) and (C2) are added in finding the (l)th smallest k-subset, which increases the difficulty greatly. In addition, it is proved in [11] that condition xi ∈ {0, 1} in solving kWTA can be relaxed to xi ∈ [0, 1] . However, this integer condition in optimization problem(2) can not be relaxed. On the other hand, for the kWTA problem, only one round of optimization should be solved.

It was shown that the coupled neurons could achieve synchronization with the variation of coupling strength. It has been suggested that the variation of spike train pattern and neural synchronization can provide some important guidelines to understanding the process of neural information Spike Train Pattern and Firing Synchronization in a Model 35 transmission. In the olfactory system particular, these analysis maybe helpful to understand the integration of the many factors influencing the construction and transformation of odor representations.

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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.)


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