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Continuous Attractors of Lotka-Volterra Recurrent Neural Networks

  • Haixian Zhang
  • Jiali Yu
  • Zhang Yi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)

Abstract

Continuous attractor neural network (CANN) models have been studied in conjunction with many diverse brain functions including local cortical processing, working memory, and spatial representation. There is good evidence for continuous stimuli, such as orientation, moving direction, and the spatial location of objects could be encoded as continuous attractors in neural networks. Although their wide applications for the information processing in the brain, representation and stability analysis of continuous attractors in non-linear recurrent neural networks (RNNs) have been reported very little so far. This paper studies the continuous attractors of Lotka-Volterra (LV) recurrent neural networks. Conditions are given to insure the network has continuous attractors. Representation of continuous attractor is obtained under the conditions. Simulations are employed to illustrate the theory.

Keywords

Continuous attractors Recurrent neural networks Convergence 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Haixian Zhang
    • 1
  • Jiali Yu
    • 1
  • Zhang Yi
    • 2
  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduP.R. China
  2. 2.Machine Intelligence Laboratory, School of Computer ScienceSichuan UniversityChengduP.R. China

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