Tackling Learning Intractability Through Topological Organization and Regulation of Cortical Networks

@article{Thangavelautham2012TacklingLI,
  title={Tackling Learning Intractability Through Topological Organization and Regulation of Cortical Networks},
  author={Jekanthan Thangavelautham and Gabriele M. T. D'Eleuterio},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2012},
  volume={23},
  pages={552-564}
}
A key challenge in evolving control systems for robots using neural networks is training tractability. Evolving monolithic fixed topology neural networks is shown to be intractable with limited supervision in high dimensional search spaces. Common strategies to overcome this limitation are to provide more supervision by encouraging particular solution strategies, manually decomposing the task and segmenting the search space and network. These strategies require a supervisor with domain… CONTINUE READING

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A regulatory theory of cortical organization and its applications to robotics

  • J. Thangavelautham
  • Ph.D. thesis, Inst. Aerospace Studies, Univ…
  • 2008
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