[Illinois]: Kohonen self-organizing map (SOM) algorithm

By Bara Saadah; John Feser1; NanoBio Node1; Jessica S Johnson1

1. University of Illinois at Urbana-Champaign

This too implements the Kohonen self-organizing map (SOM) algorithm

Launch Tool

You must login before you can run this tool.

Version 1.3b - published on 06 Aug 2014

doi:10.4231/D38P5V98N cite this

This tool is closed source.

View All Supporting Documents

    Tonopicsom

Category

Tools

Published on

Abstract

Response of the output units in a 20-by-10 network trained using the SOM to form a tonotopic map. The 20,broadly tuned input units provide a distributed encoding of the frequency of stimulated sounds(as shown for two frequencies within the range in Figure 5.10). The responses of each of the 10 output units to this input at every test frequency are plotted in two-dimensions with overlain curves(A), One for each output unit, or with curves that are spread out in a three-dimensional contour plot(B).

Sponsored by

References

Anastasio, Thomas J. Tutorial on Neural Systems Modeling. Sunderland: Sinauer Associates, 2010. Print.

Cite this work

Researchers should cite this work as follows:

  • Bara Saadah, John Feser, NanoBio Node, Jessica S Johnson (2014), "[Illinois]: Kohonen self-organizing map (SOM) algorithm," https://nanohub.org/resources/tonotopicsom. (DOI: 10.4231/D38P5V98N).

    BibTex | EndNote

Tags

  1. nano/bio