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A Neural Dynamic Architecture Resolves Phrases about Spatial Relations in Visual Scenes

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8681))

Abstract

How spatial language, important to both cognitive science and robotics, is mapped to real-world scenes by neural processes is not understood. We present an autonomous neural dynamics that achieves this mapping flexibly. Neural activation fields represent and spatially transform perceptual information. An architecture of dynamic nodes interacts with these perceptual fields to instantiate categorical concepts. Discrete time processing steps emerge from instabilities of the time-continuous neural dynamics and are organized sequentially by these nodes. These steps include the attentional selection of individual objects in a scene, mapping locations to an object-centered reference frame, and evaluating matches to relational spatial terms. The architecture can respond to queries specified by setting the state of discrete nodes. It autonomously generates a response based on visual input about a scene.

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© 2014 Springer International Publishing Switzerland

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Richter, M., Lins, J., Schneegans, S., Schöner, G. (2014). A Neural Dynamic Architecture Resolves Phrases about Spatial Relations in Visual Scenes. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_26

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  • DOI: https://doi.org/10.1007/978-3-319-11179-7_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11178-0

  • Online ISBN: 978-3-319-11179-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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