A Communicative Model: Can We Interpret Neural Dynamics of Understanding?

Conference paper

Abstract

In this paper, a communicative model with two nonequilibrium neural networks is proposed to emulate the dynamical process of how we can understand each other. A novelty-induced learning process is introduced to realize memory transmission between heterogeneous neural network models. The simulation results suggest that the communicative model could subserve to interpret the underlying neural mechanism of understanding.

Keywords

Pyramidal Neuron Memory Retrieval Communicative Model Mirror Neuron Hopfield Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was partially supported by a Grant-in-Aid for Scientific Research on Innovative Areas (No.4103)(21120002) from MEXT in Japan and partially supported by HFSPO(HFSP:RGP0039).

References

  1. 1.
    Christian Keysers and Valeria Gazzola. Social neuroscience: Mirror neurons recorded in humans. Current biology, 20(8):353–354, 2010.CrossRefGoogle Scholar
  2. 2.
    Marleen B. Schippers, Alard Roebroeck, Remco Renken, Luca Nanetti, and Christian Keysers. Mapping the information flow from one brain to another during gestural communication. Proceedings of the National Academy of Sciences, 107(20):9388–9393, 2010.CrossRefGoogle Scholar
  3. 3.
    Greg J. Stephens, Lauren J. Silbert, and Uri Hasson. Speaker-listener neural coupling underlies successful communication. Proceedings of the National Academy of Sciences, 2010.Google Scholar
  4. 4.
    C. A. Skarda and W. J. Freeman. Brains make chaos to make sense of the world. Behavioral and Brain Sciences, 10(2):161–173, 1987.CrossRefGoogle Scholar
  5. 5.
    I. Tsuda, E. Koerner, and H. Shimizu. Memory dynamics in asynchronous neural networks. Progress of Theoretical Physics, 78(1):51–71, 1987.CrossRefGoogle Scholar
  6. 6.
    Ichiro Tsuda. Dynamic link of memory–chaotic memory map in nonequilibrium neural networks. Neural Networks, 5(2):313–326, 1992.CrossRefGoogle Scholar
  7. 7.
    L. Nyberg. Any novelty in hippocampal formation and memory? Current Opinion in Neurology, 18(4):424–428, 2005.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Research Center for Integrative MathematicsHokkaido UniversitySapporoJapan
  2. 2.Research Institute for Electronic ScienceHokkaido UniversitySapporoJapan

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