Dynamical Systems and Accurate Temporal Information Transmission in Neural Networks

  • Alessandro E.P. Villa
  • Yoshiyuki Asai
  • Javier Iglesias
  • Olga K. Chibirova
  • Jérémie Cabessa
  • Pierre Dutoit
  • Vladyslav Shaposhnyk
Conference paper

Abstract

We simulated the activity of hierarchically organized spiking neural networks characterized by an initial developmental phase featuring cell death followed by spike timing dependent synaptic plasticity in presence of background noise. Upstream networks receiving spatiotemporally organized external inputs projected to downstream networks disconnected from external inputs. The observation of precise firing sequences, formed by recurrent patterns of spikes intervals above chance levels, suggested the build-up of an unsupervised connectivity able to sustain and preserve temporal information processing.

Keywords

Spike Train Neural Stem Cell Proliferation Spike Timing Dependent Plasticity Temporal Information Processing Output Spike Train 
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

Acknowledgments

The authors ackowledge the support by the EU FP6 grants #034632 PERPLEXUS and #043309 GABA.

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Alessandro E.P. Villa
    • 1
    • 2
    • 3
  • Yoshiyuki Asai
    • 4
  • Javier Iglesias
    • 1
  • Olga K. Chibirova
    • 1
  • Jérémie Cabessa
    • 1
    • 3
  • Pierre Dutoit
    • 1
  • Vladyslav Shaposhnyk
    • 1
  1. 1.Neuroheuristic Research GroupGrenoble Institute of Neuroscience, Université Joseph FourierGrenobleFrance
  2. 2.Department of Psychiatry HUGESleep Research LaboratoryGenevaSwitzerland
  3. 3.Neuroheuristic Research GroupISI-HEC, University of LausanneLausanneSwitzerland
  4. 4.Center for Advanced Medical Engineering and Informatics, Osaka UniversityOsakaJapan

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