By Long Cheng, Qingshan Liu, Andrey Ronzhin
This publication constitutes the refereed court cases of the thirteenth foreign Symposium on Neural Networks, ISNN 2016, held in St. Petersburg, Russia in July 2016. The eighty four revised complete papers provided during this quantity have been conscientiously reviewed and chosen from 104 submissions. The papers hide many issues of neural network-related learn together with sign and photo processing; dynamical behaviors of recurrent neural networks; clever keep an eye on; clustering, category, modeling, and forecasting; evolutionary computation; and cognition computation and spiking neural networks.
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Additional info for Advances in Neural Networks – ISNN 2016: 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russia, July 6-8, 2016, Proceedings
The ﬁrst and second column shows the original image and the ground truth respectively. The third and forth column shows the raw output of our CNN and sketch token  respectively. 18 R. Wang Fig. 7. Learned ﬁlters in the ﬁrst convolutional layer. Table 3 compares the performance when using diﬀerent network structures. We tried a complex network architecture consisting of three convolutional layers, three pooling layers and two fully connected layers. However, this complex structure can hardly improve the performance while it takes much longer time on training and overﬁts easily.
Then, they were selected and projected back to the electrode ﬁeld. The projected waveforms at FZ and FCz and the topography are shown in Figs. 7a and 7b for ICA on the responses of deviant /ga/. Regarding the applications of ICA on responses of deviant /da/ and standard /ba/ separately, the satisfactory temporal components of interest with the dipolar topographies were also extracted, and were selected to project back to the electrode ﬁeld. The extracted components are not shown due to the limited length of the paper.
In order to select an optimal network structure, we performed a lot of experiments on the popular BSDS500  dataset, comparing diﬀerent network structures as well as data combination, preprocessing and post-processing techniques (see Sects. 2 and 3 for detail). The best performance is achieved on a simple three-layer network taking raw RGB color image patches as input without any preprocessing. By adding non-maximum suppression to the whole system, the performance can be further improved a little.