Abstract
The dynamical organization or “binding” of elementary constituents into larger structures forms an essential operation for most information processing systems. Synchronization of temporal oscillations has been proposed as a major mechanism to achieve such organization in neural networks. We present an alternative approach, based on the competitive dynamics of tonic neurons in a layered network architecture. We discuss some properties of the resulting “Competitive Layer Model (CLM)” system and show that the proposed dynamics can give rise to Gestalt-like grouping operations for visual patterns and can model some characteristics of human visual perception. Finally, we report on an approach how the necessary, task-dependent interactions can be formed by learning from labeled grouping examples and sketch as an application of the system segmentation of biomedical images.
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Ritter, H., Weng, S., Ontrup, J., Steil, J. (2007). Gestalt Formation in a Competitive Layered Neural Architecture. In: Feng, J., Jost, J., Qian, M. (eds) Networks: From Biology to Theory. Springer, London. https://doi.org/10.1007/978-1-84628-780-0_8
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DOI: https://doi.org/10.1007/978-1-84628-780-0_8
Publisher Name: Springer, London
Print ISBN: 978-1-84628-485-4
Online ISBN: 978-1-84628-780-0
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