LISSOM: Laterally Interconnected Self-Organizing Maps
Active from 1987 - 1997
LISSOM is a biologically more realistic implementation of the SOM idea, where the weight change neighborhood is determined through competition and collaboration mediated by lateral connections (instead of a global supervisor), and weights are changed based on Hebbian learning and renormalization (instead of Euclidean distance). LISSOM was developed as a first step towards modeling biological maps (see the visual cortex page), but it also has useful properties in its own right. It is capable of self-organization roughly similar to the SOM model, but because the lateral connections decorrelate the activation patterns on the map, they form a better internal representation for visual patterns such as those in handwritten digit recognition.
Joseph Sirosh Ph.D. Alumni joseph sirosh [at] gmail com
Yoonsuck Choe Ph.D. Alumni choe [at] tamu edu
Risto Miikkulainen Faculty risto [at] cs utexas edu
Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex James A. Bednar Masters Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1997... 1997

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