Download e-book for kindle: Advances in Neural Networks – ISNN 2013: 10th International by Quansheng Ren, Qiufeng Long, Zhiqiang Zhang, Jianye Zhao

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 complaints 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 quite a few 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 equipment and SVM, optimization algorithms, varational equipment, keep watch over, robotics, bioinformatics and biomedical engineering, brain-like structures and brain-computer interfaces, info mining and data discovery and different functions of neural networks.

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Read or Download Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part I PDF

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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

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2. 2). while varying the maximal conductance of N a current in the model, the frequency of spike firing do not have large change. 3. The value of voltage will be stabilized at 40mv when the gN a is larger than 50μS/cm2 . 3 shows the result taking gN a as linear function of time t. So it is supposed to simulate the experimental phenomenon through controlling the maximal conductance of the model. 2 Effect of Multi-Maximal Conductances to the Spike Train Pattern of Olfactory Model If changing the maximal conductances of the N ap and Ks currents simultaneously, the variation tendency of spike train patterns are different to the case that only change one parameter.

Phys. Rev. E 74, 045101 (2006) 14. : Evolution of Network Structure by Temporal Learning. Phys. A 388, 1959–1966 (2009) 15. : Self-Organization of Feed-Forward Structure and Entrainment in Excitatory Neural Networks with Spike-Timing-Dependent Plasticity. Phys. 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.

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. : Competitive Hebbian Learning Through SpikeTiming-Dependent Synaptic Plasticity.

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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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