eLoom and Flatland: specification, simulation and visualization engines for the study of arbitrary hierarchical neural architectures

Authors:
Thomas P. Caudell;Yunhai Xiao;Michael J. Healy
Affiliations:
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM;Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM;University of New Mexico and University of Washington, 13544 23rd Place NE, Seattle, WA
Venue:
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Year:
2003

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Abstract

eLoom is an open source graph simulation software tool, developed at the University of New Mexico (UNM), than enables users to specify and simulate neural network models. Its specification language and libraries enables users to construct and simulate arbitrary, potentially hierarchical network structures on serial and parallel processing systems. In addition, eLoom is integrated with UNM's Flatland, an open source virtual environments development tool to provide real-time visualizations of the network structure and activity. Visualization is a useful method for understanding both learning and computation in artificial neural networks. Through 3D animated pictorially representations of the state and flow of information in the network, a better understanding of network functionality is achieved. ART-1, LAPART-II, MLP, and SOM neural networks are presented to illustrate eLoom and Flatland's capabilities.