Advances in Neural Networks - ISNN 2010: 7th International by Longwen Huang, Si Wu (auth.), Liqing Zhang, Bao-Liang Lu,
By Longwen Huang, Si Wu (auth.), Liqing Zhang, Bao-Liang Lu, James Kwok (eds.)
This booklet and its sister quantity acquire refereed papers offered on the seventh Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. development at the good fortune of the former six successive ISNN symposiums, ISNN has develop into a well-established sequence of renowned and top quality meetings on neural computation and its functions. ISNN goals at offering a platform for scientists, researchers, engineers, in addition to scholars to collect jointly to offer and speak about the most recent progresses in neural networks, and functions in varied components. these days, the sector of neural networks has been fostered a long way past the conventional synthetic neural networks. This 12 months, ISNN 2010 got 591 submissions from greater than forty international locations and areas. in keeping with rigorous stories, a hundred and seventy papers have been chosen for booklet within the complaints. The papers accumulated within the complaints conceal a vast spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and functions of neural networks. we have now geared up the papers into volumes according to their subject matters. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the subsequent issues: neurophysiological starting place, idea and types, studying and inference, neurodynamics. the second one quantity en- tled “Advance in Neural Networks ISNN 2010, half 2” covers the next 5 subject matters: SVM and kernel tools, imaginative and prescient and photo, information mining and textual content research, BCI and mind imaging, and applications.
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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. construction at the good fortune of the former six successive ISNN symposiums, ISNN has develop into a well-established sequence of renowned and top quality meetings on neural computation and its functions.
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Extra 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
Amel Grissa Touzi 625 Author Index . . . . . . . . . . . . . . . . . . . . . . . . . 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.
This is critical for fast computation. It implies that the stationary distribution of membrane potentials of the network is invariant with respect to the change of external inputs. 1 Population Dynamics of Model 1 Denote r the ﬁring rate of each neuron. With the mean-ﬁeld approximation, we calculate the mean and the variance of recurrent input to a neuron, which are < m e−(t−tj wij )/τs > ≈ Np m j 1 < Np t −∞ e−(t−t )/τs dW > = rτs , D( m e−(t−tj wij j m /τs )= Np D( (N p)2 (9) t −∞ e−(t−t )/τs dW ) ≈ 0, (10) where dW denotes a diﬀusion approximation of the Poisson process and the symbol D(x) the variance of x.
ISNN 2010, Part I, LNCS 6063, pp. 9–16, 2010. c Springer-Verlag Berlin Heidelberg 2010 (2) 10 M. Xiao and J. Cao where μ > 0, a > 0, τ ≥ 0 is the time delay and b(τ ) > 0, which is called memory function, is a strictly decreasing function of τ . The presence of such dependence often greatly complicates the task of an analytical study of such model. Most existing methods for studying bifurcation fail when applied to such a class of delay models. Compared with the intensive studies on the neural networks with delayindependent parameters, little progress has been achieved for the systems that have delay-dependent parameters.