Combining Pixelization and Dimensional Stacking

  • John T. Langton
  • Astrid A. Prinz
  • Timothy J. Hickey
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4292)


The combination of pixelization and dimensional stacking yields a highly informative visualization that uniquely facilitates feature discovery and exploratory analysis of multidimensional, multivariate data. Pixelization is the mapping of each data point in some set to a pixel in an image. Dimensional stacking is a layout method where N dimensions are projected into 2. We have combined both methods to support visual data mining of a vast neuroscience database. Images produced from this approach have now appeared in the Journal of Neurophysiology [1] and are being used for educational purposes in neuroscience classes at Emory University. In this paper we present our combination of dimensional stacking and pixelization, our extensions to these methods, and how our techniques have been used in neuroscience investigations.


Model Neuron Data Trend Dimension Order Conductance Parameter Silent Neuron 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • John T. Langton
    • 1
  • Astrid A. Prinz
    • 2
  • Timothy J. Hickey
    • 3
  1. 1.Charles River Analytics 
  2. 2.Department of BiologyEmory University 
  3. 3.Computer Science DepartmentBrandeis University 

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