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)


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.


Excitatory Neuron Articulation Point Prefer Pair Maximal Pair Excitatory Population 
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  1. 1.
    Somers, D.C., Nelson, S.B., Sur, M.: An Emergent Model of Orientation Selectivity in Cat Visual Cortical Simple Cells. J. Neurosci. 15, 5448–5465 (2005)Google Scholar
  2. 2.
    Troyer, T.W., Krukowski, A.E., Priebe, N.J., Miller, K.D.: Contrast-Invariant Orientation Tuning in Cat Visual Cortex: Thalamocortical Input Tuning and Correlation-Based Intracortical Activity. J. Neurosci. 18, 5908–5927 (1998)Google Scholar
  3. 3.
    Lauritsen, T.Z., Miller, K.D.: Different Roles for Simple-Cell and Complex-Cell Inhibition. J. Neurosci. 23, 10201–10213 (2003)Google Scholar
  4. 4.
    Ito, M., Komatsu, H.: Representation of Angles Embedded within Contour Stimuli in Area V2 of Macaque Monkeys. J. Neurosci. 24, 3313–3324 (2004)CrossRefGoogle Scholar
  5. 5.
    Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nature Neurosci. 2, 1019–1025 (1999)CrossRefGoogle Scholar
  6. 6.
    Riesenhuber, M., Poggio, T.: Models of object recognition. Nature Neurosci. (suppl. 3), 1199–1204 (2000)Google Scholar
  7. 7.
    Giese, M., Leopold, D.A.: Physiologically inspired neural model for the encoding of face spaces (2004) (preprint) Google Scholar
  8. 8.
    Fukushima, K.: Neocognitron capable of incremental learning. Neural Networks 17, 37–46 (2004)zbMATHCrossRefGoogle Scholar
  9. 9.
    Deco, G., Rolls, E.: Object-based visual neglect: a computational hypothesis. Eur. J. Neurosci. 16, 1994–2000 (2002)CrossRefGoogle Scholar
  10. 10.
    Van der Velde, F., de Kamps, M.: From Knowing What to Knowing Where: Modeling Object-based Attention with Feedback Disinhibition of Activation. J. Cog. Neursoci. 13, 479–491 (2001)CrossRefGoogle Scholar
  11. 11.
    Pasupathy, A., Connor, C.E.: Shape Representation in Area V4: Position-Specific Tuning for Boundary Configuration. J. Neurophysiol. 86, 2505–2519 (2001)Google Scholar
  12. 12.
    Hegde, J., Van Essen, D.C.: Selection for Complex Shapes in Primate Visual Area V2. J. Neurosci. 20(RC61), 1–6 (2000)Google Scholar
  13. 13.
    Felleman, D.J., van Essen, D.C.: Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex 1, 1–47 (1991)CrossRefGoogle Scholar
  14. 14.
    Bi, G.-Q., Poo, M.-M.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18, 10464–10472 (1998)Google Scholar
  15. 15.
    Dan, Y., Poo, M.-M.: Spike Timing-Dependent Plasticity of Neural Circuits. Neuron 44, 23–30 (2004)CrossRefGoogle Scholar
  16. 16.
    Taylor, J., Hartley, M., Taylor, N.: Attention as Sigma-Pi Controlled Ach-Based Feedback. In: IJCNN 2005 (2005) (submitted)Google Scholar

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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