By Longwen Huang, Si Wu (auth.), Liqing Zhang, Bao-Liang Lu, James Kwok (eds.)
This publication 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 good fortune of the former six successive ISNN symposiums, ISNN has turn into a well-established sequence of well known and top of the range meetings on neural computation and its purposes. ISNN goals at offering a platform for scientists, researchers, engineers, in addition to scholars to collect jointly to offer and talk about the newest progresses in neural networks, and purposes in various parts. these days, the sphere of neural networks has been fostered a long way past the conventional man made neural networks. This 12 months, ISNN 2010 got 591 submissions from greater than forty international locations and areas. in keeping with rigorous studies, a hundred and seventy papers have been chosen for booklet within the court cases. The papers accumulated within the lawsuits conceal a large spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and functions of neural networks. we've got geared up the papers into volumes in response to their issues. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the next subject matters: neurophysiological starting place, concept 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 tools, imaginative and prescient and picture, 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
Vis. Neurosci. 10, 811–825 (1993) 3. : Global and Fine Information Coded by Single Neurons in the Temporal Visual Cortex. Nature 400, 869–873 (1999) 4. : Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous State and Locking. Neural Computation 12, 43–89 (1999) 5. : Fast Propagation of Firing Rates through Layered Networks of Neurons. J. Neurosci. 22, 1956–1966 (2002) 6. : Introduction to Theoretical Neurobiology. Cambridge University Press, Cambridge (1996) 7. : Fast Global Oscillations in Networks of Integarte-and-Fire Neurons with Low Firing Rates.
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.
CDS: coding sequences. 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.