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Spatio-temporal Dynamics during Perceptual Processing in an Oscillatory Neural Network

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5164))

Abstract

We examine the dynamics of object recognition in a multi-layer network of oscillatory elements. The derivation of network dynamics is based on principles of sparse representation, and results in system behavior that achieves binding through phase synchronization. We examine the behavior of the network during recognition of objects with missing contours. We observe that certain network units respond to missing contours with reduced amplitude and temporal delay, similar to neuroscientific findings. Furthermore, these units maintain synchronization with a high-level object representation only in the presence of feedback.

Our results suggest that the illusory contour phenomena are formal consequences of a system that dynamically solves the binding problem, and highlight the functional relevance of feedback connections.

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References

  1. Wang, D.: The time dimension for scene analysis. IEEE Trans. Neural Networks 16(6), 1401–1426 (2005)

    Article  Google Scholar 

  2. Rao, A.R., Cecchi, G.A., Peck, C.C., Kozloski, J.R.: Unsupervised segmentation with dynamical units. IEEE Trans. Neural Networks (January 2008)

    Google Scholar 

  3. Rao, A.R., Cecchi, G.A., Peck, C.C., Kozloski, J.R.: Efficient segmentation in multi-layer oscillatory networks. In: IJCNN (accepted, 2008)

    Google Scholar 

  4. Lee, T.S., Mumford, D.: Hierarchical bayesian inference in the visual cortex. J. Optical Soc. America A 20(7), 1434–1448 (2003)

    Article  Google Scholar 

  5. Grossberg, S., Mingolla, E.: Neural dynamics of perceptual grouping: textures, boundaries and emergent segmentations. Perception and Psychophysics 38, 141–171

    Google Scholar 

  6. Engel, A., Pascal, F., Singer, W.: Dynamic predictions: oscillations and synchrony in top-down processing. Nature Reviews Neuroscience 2, 704–716 (2001)

    Article  Google Scholar 

  7. Bar, M., et al.: Top-down facilitation of visual recognition. PNAS 103(2), 449–454 (2006)

    Article  MathSciNet  Google Scholar 

  8. Li, Z.: A neural model of contour integration in the primary visual cortex. Neural Compuation 10, 903–940 (1998)

    Article  Google Scholar 

  9. Fukushima, K., Miyake, S., Ito, T.: Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Trans. Systems, Man, and Cybernetics 13(3), 826–834 (1983)

    Google Scholar 

  10. Galuske, R., et al.: The role of feedback representations in cat visual cortex. PNAS 99(26), 17083–17088 (2002)

    Article  Google Scholar 

  11. Lamme, V., Roelfsema, P.: The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neuroscience 23, 571–579 (2000)

    Article  Google Scholar 

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Véra Kůrková Roman Neruda Jan Koutník

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© 2008 Springer-Verlag Berlin Heidelberg

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Rao, A.R., Cecchi, G. (2008). Spatio-temporal Dynamics during Perceptual Processing in an Oscillatory Neural Network. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_71

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  • DOI: https://doi.org/10.1007/978-3-540-87559-8_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87558-1

  • Online ISBN: 978-3-540-87559-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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