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A Recurrent Multimodal Network for Binding Written Words and Sensory-Based Semantics into Concepts

  • Conference paper
Neural Information Processing (ICONIP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7062))

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Abstract

We present a recurrent multimodal model of binding written words to mental objects and investigate the capability of the network in reading misspelt but categorically related words. Our model consists of three mutually interconnected association modules which store mental objects, represent their written names and bind these together to form mental concepts. A feedback gain controlling top-down influence is incorporated into the model architecture and it is shown that correct settings for this during map formation and simulated reading experiments is necessary for correct interpretation and semantic binding of the written words.

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Papliński, A.P., Gustafsson, L., Mount, W.M. (2011). A Recurrent Multimodal Network for Binding Written Words and Sensory-Based Semantics into Concepts. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_50

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  • DOI: https://doi.org/10.1007/978-3-642-24955-6_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24954-9

  • Online ISBN: 978-3-642-24955-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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