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Conference Paper

Learning Multiple Feature Representations from Natural Image Sequences

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84006

Kayser,  C
Research Group Physiology of Sensory Integration, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Einhäuser, W., Kayser, C., Kording, K., & König, P. (2002). Learning Multiple Feature Representations from Natural Image Sequences. Artificial Neural Networks: ICANN 2002, 21-26.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E072-5
Abstract
Hierarchical neural networks require the parallel extraction of multiple features. This raises the question how a subpopulation of cells can become specific to one feature and invariant to another, while a different subpopulation becomes invariant to the first but specific to the second feature. Using a colour image sequence recorded by a camera mounted to a cat’s head, we train a population of neurons to achieve optimally stable responses. We find that colour sensitive cells emerge. Adding the additional objective of decorrelating the neurons’ outputs leads a subpopulation to develop achromatic receptive fields. The colour sensitive cells tend to be non-oriented, while the achromatic cells are orientation-tuned, in accordance with physiological findings. The proposed objective thus successfully separates cells which are specific for orientation and invariant to colour from orientation invariant colour cells.