Advances in neural networks - ISNN 2008 5th International by Fuchun Sun, Jianwei Zhang, Jinde Cao, Wen Yu

By Fuchun Sun, Jianwei Zhang, Jinde Cao, Wen Yu

The quantity set LNCS 5263/5264 constitutes the refereed court cases of the fifth foreign Symposium on Neural Networks, ISNN 2008, held in Beijing, China in September 2008.

The 192 revised papers provided have been conscientiously reviewed and chosen from a complete of 522 submissions. The papers are equipped in topical sections on computational neuroscience; cognitive technology; mathematical modeling of neural platforms; balance and nonlinear research; feedforward and fuzzy neural networks; probabilistic tools; supervised studying; unsupervised studying; aid vector computing device and kernel equipment; hybrid optimisation algorithms; desktop studying and information mining; clever regulate and robotics; trend acceptance; audio photograph processinc and desktop imaginative and prescient; fault analysis; purposes and implementations; purposes of neural networks in digital engineering; mobile neural networks and complicated regulate with neural networks; nature encouraged  tools of high-dimensional discrete facts research; development acceptance and data processing utilizing neural networks.

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Extra info for Advances in neural networks - ISNN 2008 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, September 24-28, 2008: proceedings

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Considering the delay, through Hebbian learning the connections from layer III to layer IV can associate a sub-component to the next. One sub-component may link multiple following sub-components plus itself, but the prediction is deterministic by a mechanism involving competition and threshold dynamic. So instead of learning the whole sequence, minicolumns selectively extract information. Information for complex concepts are distributed in multiple minicolumns, and long time thinking are in the form of integrated dynamics in the whole cortex, including recurrent activity.

Much of research on auditory model has already shown that the features based on simulation of auditory system are more robust than traditional features under noisy background. In our feature extraction framework, we calculate the frequency selectivity information by imitating the process performed in the auditory periphery and pathway. And the robust speech features are obtained by the projections of the extracted auditory information mapped into multiple interrelated feature subspace via cNTF.

However, the selection of relevant frequency band and the interpretation of the results in frequency domain is a challenging task. In this paper, we first estimated single-trial ERPs during an emotional processing based on wavelet multiresolution analysis (MRA) [15], and then analyzed the brain responses to emotional stimuli by using the extracted ERPs. The results indicated the effectiveness of the wavelet transform-based approach in ERP single-trial extraction and further supported the view that emotional stimuli are processed more intensely.

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