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Parallels between Machine and Brain Decoding

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Brain Informatics (BI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7670))

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Abstract

We report some existing work, inspired by analogies between human thought and machine computation, showing that the informational state of a digital computer can be decoded in a similar way to brain decoding. We then discuss some proposed work that would leverage this analogy to shed light on the amount of information that may be missed by the technical limitations of current neuroimaging technologies.

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Author information

Authors and Affiliations

  1. Artificial Intelligence Research, University of Rome Tor Vergata, Rome, Italy

    Lorenzo Dell’Arciprete & Fabio Massimo Zanzotto

  2. Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA

    Brian Murphy

Authors
  1. Lorenzo Dell’Arciprete
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  2. Brian Murphy
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  3. Fabio Massimo Zanzotto
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Editor information

Editors and Affiliations

  1. Department of Enterprise Engineering, University of Rome “Tor Vergata”, Viale del Politecnico, 1, 00133, Roma, Italy

    Fabio Massimo Zanzotto

  2. Department of Medical Informatics, Faculty of Medicine, Shimane University, 89-1 Enya-cho, 693-8501, Izumo-city, Shimane, Japan

    Shusaku Tsumoto

  3. Department of Artificial Intelligence, Faculty of Mathematics and Natural Sciences, University of Groningen, Nijenborgh 9, 9747 AG, Groningen, Netherlands

    Niels Taatgen

  4. Department of Computer Science, University of Regina, 3737 Wascana Parkway, S4S 0A2, Regina, SK, Canada

    Yiyu Yao

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© 2012 Springer-Verlag Berlin Heidelberg

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Dell’Arciprete, L., Murphy, B., Zanzotto, F.M. (2012). Parallels between Machine and Brain Decoding. In: Zanzotto, F.M., Tsumoto, S., Taatgen, N., Yao, Y. (eds) Brain Informatics. BI 2012. Lecture Notes in Computer Science(), vol 7670. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35139-6_16

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  • DOI: https://doi.org/10.1007/978-3-642-35139-6_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35138-9

  • Online ISBN: 978-3-642-35139-6

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