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SORN: a self-organizing recurrent neural network.

Andreea Lazar1, Gordon Pipa, Jochen Triesch

  • 1Frankfurt Institute of Advanced Studies, Johann Wolfgang Goethe University Frankfurt am Main, Germany. lazar@fias.uni-frankfurt.de

Frontiers in Computational Neuroscience
|November 7, 2009
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Summary

This study introduces a self-organizing recurrent network (SORN) that mimics brain information processing. SORN utilizes three plasticity types to learn spatio-temporal patterns, enabling effective learning in artificial neural networks.

Keywords:
intrinsic plasticityrecurrent neural networksreservoir computingsynaptic plasticitytime series prediction

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Recurrent neural networks (RNNs) are crucial for understanding brain information processing.
  • Neocortical plasticity mechanisms enable RNNs to learn time-varying sensory stimuli.
  • Mimicking these biological learning abilities in artificial neural networks remains challenging.

Purpose of the Study:

  • To introduce a novel self-organizing recurrent network (SORN) model.
  • To enable artificial neural networks to learn spatio-temporal patterns effectively.
  • To maintain network dynamics within a healthy, learning-suitable regime.

Main Methods:

  • Development of SORN, a self-organizing recurrent network.
  • Integration of three distinct local plasticity mechanisms within SORN.
  • Analysis of SORN's ability to learn spatio-temporal patterns and encode information.

Main Results:

  • SORN successfully learns spatio-temporal patterns from input data.
  • The network maintains stable dynamics conducive to continuous learning.
  • Information encoding in SORN occurs via trajectories in its high-dimensional state space, aligning with cortical coding findings.

Conclusions:

  • The developed SORN model effectively learns complex spatio-temporal information.
  • All three integrated plasticity mechanisms are essential for SORN's successful operation.
  • SORN provides a biologically plausible model for information processing in recurrent neural networks.