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Visual analytics for neuroscience-inspired dynamic architectures.

Margaret Drouhard, Catherine D. Schuman, J. Douglas Birdwell, and Mark E. Dean.

December, 2014

FOCI: IEEE Symposium on Foundations of Computational Intelligence

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6999009

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Abstract

We introduce a visual analytics tool for neuroscience-inspired dynamic architectures (NIDA), a network type that has been previously shown to perform well on control, anomaly detection, and classification tasks. NIDA networks are a type of spiking neural network, a non-traditional network type that captures dynamics throughout the network. We demonstrate the utility of our visualization tool in exploring and understanding the structure and activity of NIDA networks. Finally, we describe several extensions to the visual analytics tool that will further aid in the development and improvement of NIDA networks and their associated design method.

Citation Information

Text


M. Drouhard, C. D. Schuman, J. D. Birdwell and M. E. Dean, "Visual
   analytics for neuroscience-inspired dynamic architectures,"
   Foundations of Computational Intelligence (FOCI), 2014 IEEE
   Symposium on, Orlando, FL, 2014, pp. 106-113.
doi: 10.1109/FOCI.2014.7007814
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7007814&isnumber=7007795

Bibtex


@INPROCEEDINGS{dsb:14:van,
  author = "M. Drouhard and C. D. Schuman and J. D. Birdwell and M. E. Dean",
  title = "Visual Analytics for Neuroscience-Inspired Dynamic Architectures",
  booktitle = "IEEE Symposium on Foundations of Computational Intelligence (FOCI)",
  month = "December",
  yeae = "2014",
  pages = "206-113",
  address = "Orlando, FL",
  doi = "10.1109/FOCI.2014.7007814"
  where = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7007814&isnumber=7007795"
}