Download PDF by Quansheng Ren, Qiufeng Long, Zhiqiang Zhang, Jianye Zhao: Advances in Neural Networks – ISNN 2013: 10th International

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 lawsuits 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 rigorously reviewed and chosen from a variety of 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 equipment and SVM, optimization algorithms, varational tools, keep watch over, robotics, bioinformatics and biomedical engineering, brain-like platforms and brain-computer interfaces, info mining and data 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

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For the first layer (i = 1), it has (n−k +1) intermediate nodes. 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].

4. 7S/m2 , gKs . 3S/m2 , where Ik is ionic current(k = N a, Kf ast, Ka, Ks, N ap). gsyn is coupling strength, Vi and Vj are voltages between adjacent neuron i and j. Here, we take i and j as 1 and 2. All parameters are the same to the ones in section 2. A correlation coefficient is introduced to measure the synchronization degree of the coupled neurons, and it is defined as follows: C= n i=1 | (Vi1 − Vi1 ) || (Vi2 − n 1 1 2 2 i=1 (Vi − Vi ) (Vi − Vi2 ) | Vi2 )2 (6) where Vi1 (or Vi2 ) represents the samplings of the membrane potential V 1 (t) (or V 2 (t)).

Rev. E 79, 051904 (2009) 16. : STDP-Driven Networks and The C. elegans Neuronal Network. Physica A 389, 3900–3914 (2010) 17. : Entropy and Information in Neural Spike Trains. Phys. Rev. Lett. 80, 197–200 (1998) 18. : STDP in Adaptive Neurons Gives Close-ToOptimal Information Transmission. Front. Comput. Neurosci. 4, 143–158 (2010) 19. : Information Theory and Neural Coding. Nat. Neurosci. 2, 947–957 (1999) 20. : Estimating Entropy Rates with Bayesian Confidence Intervals. Neural Comput. 7, 1531–1576 (2005) 21.

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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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