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 foreign Symposium on Neural Networks, ISNN 2013, held in Dalian, China, in July 2013. The 157 revised complete papers offered have been rigorously reviewed and chosen from various submissions. The papers are geared up in following themes: computational neuroscience, cognitive technology, neural community types, studying algorithms, balance and convergence research, kernel equipment, huge margin equipment and SVM, optimization algorithms, varational tools, regulate, 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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Extra info for Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part I
Branching dendritic trees and motoneuron membrane resistivity. Experimental Neurology 1(5), 491–527 (1959) 18. : Progress and perspectives in computational neuroanatomy. R. com Abstract. For a given set S of n real numbers, a k-subset means a subset of k distinct elements of S. It is obvious that there are totally Cnk diﬀerent combinations. The L smallest k-subsets sum problem is deﬁned as ﬁnding L k-subsets whose summation of subset elements are the L smallest among all possible combinations.
X) is the sigmoid function, given by 1 σ ( x) = . (3) 1 + e− x Therefore, the activation of gene is continuous change in the interval [0, 1], where 0 represents gene inactivation and 1 represents complete gene expression. 2 Developmental Method of Virtual Neurons In this paper, the recurrent genetic regulatory network control the dendritic development process, which includes 2 nodes in input layer, 10 nodes in regulatory layer, and 10 nodes in output layer. The initial genetic regulatory network can express as a soma, which activation is random initialization within the interval [0, 1].
This problem has many applications in research and the real world. However the problem is very computationally challenging. In this paper, a novel algorithm is proposed to solve this problem. By expressing all the Cnk k-subsets with a network, the problem is converted to ﬁnding the L shortest loopless paths in this network. By combining the L shortest paths algorithm and the ﬁnitetime convergent recurrent neural network, a new algorithm for the L smallest k-subsets problem is developed. And experimental results show that the proposed algorithm is very eﬀective and eﬃcient.