Download Advances in Neural Networks - ISNN 2010: 7th International by Longwen Huang, Si Wu (auth.), Liqing Zhang, Bao-Liang Lu, PDF

By Longwen Huang, Si Wu (auth.), Liqing Zhang, Bao-Liang Lu, James Kwok (eds.)

ISBN-10: 3642132774

ISBN-13: 9783642132773

This booklet and its sister quantity gather refereed papers awarded on the seventh Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. development at the luck of the former six successive ISNN symposiums, ISNN has turn into a well-established sequence of well known and top quality meetings on neural computation and its purposes. ISNN goals at supplying a platform for scientists, researchers, engineers, in addition to scholars to collect jointly to offer and talk about the most recent progresses in neural networks, and purposes in different components. these days, the sphere of neural networks has been fostered a long way past the conventional synthetic neural networks. This 12 months, ISNN 2010 obtained 591 submissions from greater than forty international locations and areas. in keeping with rigorous reports, one hundred seventy papers have been chosen for booklet within the lawsuits. The papers accrued within the lawsuits disguise a huge spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and functions of neural networks. we've got equipped the papers into volumes in line with their themes. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the subsequent issues: neurophysiological starting place, conception and versions, studying and inference, neurodynamics. the second one quantity en- tled “Advance in Neural Networks ISNN 2010, half 2” covers the next 5 issues: SVM and kernel tools, imaginative and prescient and photograph, info mining and textual content research, BCI and mind imaging, and applications.

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Additional resources for Advances in Neural Networks - ISNN 2010: 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part I

Example text

Group definition is given on Table 2. 24 G. Ji et al. respectively) are used to calculate Sn (group1~5_sn). As shown in Fig. 3, Sn and Sp results are similar among the groups containing NUE patterns (group 1 to group 4), while that of the group without NUE pattern (group 5) was significantly lower. The higher the Sn and Sp is, the better the prediction is. However, the sn and sp can not be increased at the same time, so we define a cross value which is the Y value of the intersect point of Sn and Sp curves to better evaluate our prediction results.

637 Stimulus-Dependent Noise Facilitates Tracking Performances of Neuronal Networks Longwen Huang1 and Si Wu2 1 2 Yuanpei Program and Center for Theoretical Biology, Peking University, Beijing, China Lab of Neural Information Processing, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China Abstract. Understanding why neural systems can process information extremely fast is a fundamental question in theoretical neuroscience. The present study investigates the effect of noise on speeding up neural computation.

Proof. When a = 0, λ = −μ < 0. For a > 0, clear λ = 0 is not a root of (4) since a(1 − b(τ )) < a(1 + b(τ )) ≤ μ. Let iω(ω > 0) be a root of (4), it is straightforward to obtain that ab(τ ) cos(ωτ ) = a − μ, ab(τ ) sin(ωτ ) = ω, (5) yielding ω 2 = [ab(τ )]2 −(a−μ)2 . If a ≤ μ/[1+b(τ )] holds, we have ab(τ ) ≤ |μ−a|. Thus, (4) has no imaginary root. In other words, (4) has no root appearing on the imaginary axis for a ∈ (0, μ/(1 + b(τ ))]. Recalling that the root of (4) with a = 0 has negative real part, the conclusion follows.

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Advances in Neural Networks - ISNN 2010: 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part I by Longwen Huang, Si Wu (auth.), Liqing Zhang, Bao-Liang Lu, James Kwok (eds.)


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