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Learning of Lateral Connections for Representational Invariant Recognition

Christian Keck1*, Jan Bouecke2 and Jorg Lucke1
  • 1 Frankfurt Institute for Advanced Studies, Germany
  • 2 University of Ulm, Institute of Neural Information Processing, Germany

The mammalian visual cortex is a fast and recurrent information processing system which rapidly integrates sensory information and high-level model knowledge to form a reliable percept of a given visual environment. Much is known about the local features this system is using for processing. Receptive fields of simple cells are, for instance, well described by Gabor wavelet functions. Many systems in the literature study how such Gabor wavelets can be learned from input [1,2 and many more]. In contrast, we study in this work how the lateral interaction of local Gabor features can be learned in an unsupervised way.

We study a system that builds up on recent work showing how local image features can be combined to form explicit object representations in memory (e.g., [3-7]). In these theoretical works objects in memory are represented as specific spatial arrangements of local features which are recurrently compared with feature arrangements in a given input. It was shown that this approach can be used successfully in tasks of invariant object recognition (e.g., [7,8]).

While previous work has used a pre-wired lateral connectivity for recurrent inference, and predefined object representations (compare [3-8] but see [9]) we, in this work, address the following questions: 1) How can object representations in the form of feature arrangements be learned? 2) How can the transformations that relate such memory representations to a given V1 image representation be learned?

For training, different images of the same object are shown to the studied system. Depending on the input, the system learns the arrangement of features typical for the object along with allowed object transformations. The choice of the set of training images of this object hereby determines the set of transformations the system learns.

We present new results on one and two-dimensional data sets. If trained on one-dimensional input, the system learns one-dimensional object representations along with one-dimensional translations. If trained on 2-D data, the system learns an object representation of two dimensional feature arrangements together with planar translations as allowed transformations.

Acknowledgements:This work was supported by the German Federal Ministry of Education and Research (BMBF) grant number 01GQ0840 (Bernstein Focus Neurotechnology Frankfurt).

References

1. Olshausen, B., Field, D. J., Nature 381:607-609, 1996.

2. Lucke, J., Neural Computation, in press, 2009

3. Arathorn, D., Standford Univ. Press, California, 2002.

4. Olshausen, B. A., Anderson, C. H., and Essen, D. C. V.,The Journal of Neuroscience, 13(11):4700-4719, 1993.

5. Lucke, J., Keck, C., and Malsburg, C., Neural Computation 20(10):2441-2463, 2008.

6. Wiskott, L. and von der Malsburg, C., In: Lateral Interactions in the Cortex: Structure and Function, ISBN 0-9647060-0-8, 1995.

7. Wolfrum, P., Wolff, C., Lücke, J., and von der Malsburg, C., Journal of Vision, 8(7):1-18, 2008.

8. Messer, K., et al., BANCA competition, CVPR, 523-532, 2004.

9. Bouecke, J.D., and Lücke, J., ICANN, 557-566, 2008.

Conference: Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009.

Presentation Type: Poster Presentation

Topic: Abstracts

Citation: Keck C, Bouecke J and Lucke J (2009). Learning of Lateral Connections for Representational Invariant Recognition. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.neuro.10.2009.14.012

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Received: 25 Aug 2009; Published Online: 25 Aug 2009.

* Correspondence: Christian Keck, Frankfurt Institute for Advanced Studies, Frankfurt, Germany, keck@fias.uni-frankfurt.de