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

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 technological know-how; mathematical modeling of neural structures; balance and nonlinear research; feedforward and fuzzy neural networks; probabilistic tools; supervised studying; unsupervised studying; help vector desktop and kernel equipment; hybrid optimisation algorithms; computer studying and knowledge mining; clever keep an eye on and robotics; trend popularity; audio snapshot processinc and machine imaginative and prescient; fault analysis; purposes and implementations; purposes of neural networks in digital engineering; mobile neural networks and complex keep an eye on 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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The hair-cells have receptive fields which refer to a coding of sound frequency. Here we employ the sparse localized basis function A ∈ RNf ×R in time-frequency subspace to transform the auditory feature into the sparse feature subspace, where R is the dimension of sparse feature subspace. The representation of auditory sparse feature Xs is obtained via the following transformation: ˆ Xs = AX (15) Robust Speaker Modeling Based on Constrained Nonnegative Tensor Factorization 10 2 20 1 30 0 40 2 50 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 17 1 60 0 70 2 80 1 90 100 20 40 60 (a) Basis functions 80 0 (b) Examples of encoding vector Fig.

For example in the model proposed by Korner etc. [14]. But in our view, the feedback loop along with the transmission delay is the base for associating a sub-component to the next sub-component. Notice the involved two sub-components are not input at the same time, but Hebbian learning based on the synapse plasticity requires the two involved neurons exciting at the same time [13][15-17]. This is solved by the transmission delay of this feedback loop. The synapse modification can only happen in the synaptic junction, by the changes of the amount of neurotransmitter released by the presynaptic neuron, or the number of postsynaptic receptors [15-17].

Zou et al. Fig. 3. Sample results for the time-frequency plot of a single trial of VEP. Left column corresponding the original signal. Right column corresponding the reconstructed single-trial signal by wavelet transform. noticeable in the time-frequency distribution of the wavelet-based VEP estimate, whereas such activity can hardly be seen from the raw signal. Therefore, we conclude that the wavelet-based method can recover the evoked potential. 1 (not included the VEOG and HEOG sites). For each subject the results of the wavelet decomposition of the 15 single trials were averaged, and then the grand mean visually evoked potentials (VEPs) under the three types of stimuli of the 10 subjects were obtained for 12 scalp areas.

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