Biologically Inspired Bayes Learning and Its Dependence on the Distribution of the Receptive Fields

  • Liang Wu
  • Predrag Neskovic
  • Leon N Cooper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


In this work we explore the dependence of the Bayesian Integrate And Shift (BIAS) learning algorithm on various parameters associated with designing the retina-like distribution of the receptive fields. The parameters that we consider are: the rate of increase of the sizes of the receptive fields, the overlap among the receptive fields, the size of the central receptive field, and the number of directions along which the centers of the receptive fields are placed. We show that the learning algorithm is very robust to changes in parameter values and that the recognition rates are higher when using a retina-like distribution of receptive fields compared to uniform distributions.


Receptive Field Recognition Rate Directional Distribution Circular Distribution Foveal Vision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Liang Wu
    • 1
  • Predrag Neskovic
    • 1
  • Leon N Cooper
    • 1
  1. 1.Institute for Brain and Neural Systems and Department of PhysicsBrown UniversityProvidenceUSA

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