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Bio-Inspired Computing and Applications: 7th International by YuXi Liu, ZhiFeng Zhang (auth.), De-Shuang Huang, Yong Gan,

By YuXi Liu, ZhiFeng Zhang (auth.), De-Shuang Huang, Yong Gan, Prashan Premaratne, Kyungsook Han (eds.)

The three-volume set LNCS 6838, LNAI 6839, and LNBI 6840 constitutes the completely refereed post-conference lawsuits of the seventh foreign convention on clever Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. This quantity comprises ninety three revised complete papers, from a complete of 281 displays on the convention - rigorously reviewed and chosen from 832 preliminary submissions. The papers tackle all concerns in complex clever Computing, in particular Methodologies and purposes, together with theories, methodologies, and functions in technology and expertise. They comprise quite a number ideas similar to man made intelligence, development popularity, evolutionary computing, informatics theories and functions, computational neuroscience and bioscience, gentle computing, human machine interface concerns, etc.

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Additional resources for Bio-Inspired Computing and Applications: 7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou,China, August 11-14. 2011, Revised Selected Papers

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Fig. 2. The algorithm of Parcone consists of six phases Phase 1. The number of neurons and layers is close relative with training time. When there are a lot of neurons and layers it will take lots of time to train weights. 18 D. Wang et al. Phase 2 The neural network requires input value to be [0, 1]. Therefore, 24 technical indexes and objective value called stock price are normalized to meet property of neural network. Phase 3. In the third phase, weights are initialized randomly by Random number generator.

26–31, 2012. © Springer-Verlag Berlin Heidelberg 2012 Simulation of Visual Attention Using Hierarchical Spiking Neural Networks 2 27 Spiking Neural Network for Simulation of Visual Attention Biological findings show that the visual system can use feedback signals to highlight the relevant locations[4][5]. 1, is proposed to simulate such visual attention. Suppose that a visual image is presented to the retina and an edge firing rate map [6] is obtained as the input for the network. The line detection layer contains two pathways.

It will have a close relation with many factors. The target of Parcone is to optimize weights. At first, it is important to define number of neurons and layers. Then, input variability need to be normalized since input value required to be [0, 1] in neural network. Weight value needs to be initialized. Also, it will make sure whether between neurons are connected. After that, input variability enters into neuron network. Weights will be optimized by GA. The overall framework of Parcone is shown in Fig.

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