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