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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Skeleton Model for the Neurodynamics of Visual Action Representations
verfasst von : Martin A. Giese
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2014
Verlag: Springer International Publishing
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The visual recognition of body motion in the primate brain requires the temporal integration of information over complex patterns, potentially exploiting recurrent neural networks consisting of shape- and optic-flow-selective neurons. The paper presents a mathematically simple neurodynamical model that approximates the mean-field dynamics of such networks. It is based on a two-dimensional neural field with appropriate lateral interaction kernel and an adaptation process for the individual neurons. The model accounts for a number of, so far not modeled, observations in the recognition of body motion, including perceptual multi-stability and the weakness of repetition suppression, as observed in single-cell recordings for the repeated presentation of action stimuli. In addition, the model predicts novel effects in the perceptual organization of action stimuli.