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Persistent Memories in Transient Networks

  • Andrey Babichev
  • Yuri Dabaghian
Conference paper
Part of the Springer Proceedings in Physics book series (SPPHY, volume 191)

Abstract

Spatial awareness in mammals is based on an internalized representation of the environment, encoded by large networks of spiking neurons. While such representations can last for a long time, the underlying neuronal network is transient : neuronal cells die every day, synaptic connections appear and disappear, the networks constantly change their architecture due to various forms of synaptic and structural plasticity. How can a network with a dynamic architecture encode a stable map of space? We address this question using a physiological model of a “flickering” neuronal network and demonstrate that it can maintain a robust topological representation of space.

Keywords

Simplicial Complex Cell Assembly Spike Train Betti Number Place Cell 
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

The work was supported by the NSF 1422438 grant and by Houston Bioinformatics Endowment Fund.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Jan and Dan Duncan Neurological Research Institute, Baylor College of MedicineHoustonUSA
  2. 2.Department of Computational and Applied MathematicsRice UniversityHoustonUSA

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