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Coding of Objects in Low-Level Visual Cortical Areas

  • N. R. Taylor
  • M. Hartley
  • J. G. Taylor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)

Abstract

We develop a neural network architecture to help model the creation of visual temporal object representations. We take visual input to be hard-wired up to and including V1 (as an orientation-filtering system). We then develop architectures for afferents to V2 and thence to V4, both of which are trained by a causal Hebbian law. We use an incremental approach, using sequences of increasingly complex stimuli at an increasing level of the hierarchy. The V2 representations are shown to encode angles, and V4 is found sensitive to shapes embedded in figures. These results are compared to recent experimental data, supporting the incremental training scheme and associated architecture.

Keywords

Excitatory Neuron Articulation Point Prefer Pair Maximal Pair Excitatory Population 
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 2005

Authors and Affiliations

  • N. R. Taylor
    • 1
  • M. Hartley
    • 1
  • J. G. Taylor
    • 1
  1. 1.Department of MathematicsKing’s CollegeLondonUK

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