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Neural Computations That Support Long Mixed Sequences of Knowledge Acquisition Tasks

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Theory and Applications of Models of Computation (TAMC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5532))

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Abstract

In this talk we shall first give a brief review of a quantitative approach to understanding neural computation [4-6]. We target so-called random access tasks, defined as those in which one instance of a task execution may need to access arbitrary combinations of items in memory. Such tasks are communication intensive, and therefore the known severe constraints on connectivity in the brain can inform their analysis.

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References

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Author information

Authors and Affiliations

  1. School of Engineering and Applied Sciences, Harvard University,  

    Leslie G. Valiant

Authors
  1. Leslie G. Valiant
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Texas A&M University, College Station, 77843, Texas, USA

    Jianer Chen

  2. School of Mathematics, University of Leeds, LS2 9JT, U.K.

    S. Barry Cooper

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© 2009 Springer-Verlag Berlin Heidelberg

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Valiant, L.G. (2009). Neural Computations That Support Long Mixed Sequences of Knowledge Acquisition Tasks. In: Chen, J., Cooper, S.B. (eds) Theory and Applications of Models of Computation. TAMC 2009. Lecture Notes in Computer Science, vol 5532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02017-9_1

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  • DOI: https://doi.org/10.1007/978-3-642-02017-9_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02016-2

  • Online ISBN: 978-3-642-02017-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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