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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 court cases of the seventh foreign convention on clever Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. This quantity includes ninety three revised complete papers, from a complete of 281 displays on the convention - conscientiously reviewed and chosen from 832 preliminary submissions. The papers tackle all matters in complicated clever Computing, specially Methodologies and functions, together with theories, methodologies, and purposes in technological know-how and expertise. They contain more than a few innovations similar to synthetic intelligence, development popularity, evolutionary computing, informatics theories and purposes, computational neuroscience and bioscience, delicate computing, human desktop interface concerns, etc.

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Extra 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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Visual Attention enables the visual system to process potentially important objects by selectively increasing the activity of sensory neurons that represent the relevant locations and features of the environment [3]. Given the complexity of the visual environment, the ability to selectively attend to certain locations, while ignoring others, is crucial for reducing the amount of visual information to manageable levels and for optimizing behavioral performance and response times. However, there are relatively few explanations of visual attention using spiking neural networks in the literature.

1 Introduction Alternating Projection Neural Network(APNN)[1] is firstly proposed by Marks II etc. The literature [2] studies APNN thoroughly and proposes a new neural network— Extended Alternating Projection Neural Network (EAP) which functions in the complex domain. The topology architecture and association process of EAP are the same as those of APNN. In the literature [2] the stability of EAP has been studied and strict mathematical proofs to its stability has also been given. The literature [3] obtains the mathematical expression to the steady state value of EAP and gives the sufficient and necessary condition of EAP used for Content Addressable Memory (CAM).

Neurons of EAP can be classified into clamped neurons or floating neurons according to their states. The state L si ( m) of arbitrary floating neuron i at time m equals ∑t s (m −1) , the state sj (m) pi p p=1 of arbitrary clamped neuron j at time m is equal to sj (m −1) ="= sj (0) . The weightvalue of network can be obtained by the training method in the literature [2]. Fig. 1. Illustration of EAP After EAP has been trained, it is time to decide which neurons are clamped neurons and which neurons are floating neurons.

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