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.)

This e-book and its sister quantity acquire 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 develop into a well-established sequence of well known and top quality meetings on neural computation and its purposes. ISNN goals at delivering a platform for scientists, researchers, engineers, in addition to scholars to assemble jointly to provide and speak about the newest progresses in neural networks, and purposes in different parts. these days, the sphere of neural networks has been fostered a ways past the conventional synthetic neural networks. This 12 months, ISNN 2010 acquired 591 submissions from greater than forty international locations and areas. in response to rigorous experiences, a hundred and seventy papers have been chosen for e-book within the complaints. The papers accrued within the court cases conceal a wide spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and functions of neural networks. we now have equipped the papers into volumes in line with their subject matters. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the subsequent issues: neurophysiological origin, 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 subject matters: SVM and kernel equipment, imaginative and prescient and snapshot, facts mining and textual content research, BCI and mind imaging, and applications.

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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

Example text

In a balanced network, neuronal connections are also sparse and random, however, the neuronal √ connection strength is much larger than that in Model 1. We set wij√∼ 1/ N P . The total excitatory current to a neuron is then in the order of N P , which needs to be balanced by inhibitory inputs, so that the overall recurrent input to a neuron is in the order of one. In the balanced network, the fluctuation of the overall recurrent input is in the order of one, which plays a critical role in driving the network dynamics.

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.

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. Combining with the external input, the dynamics of a single neuron is written as, dvi = −vi + (μ + rτs ) + σξi . (11) τ dt Thus, under the mean-field approximation, the effect of the recurrent interaction is equivalent to changing the mean of the synaptic input to a neuron from μ to μ + rτs .

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