The impact of network topology on self-organizing maps

In this paper, we study instances of complex neural networks, i.e. neural networks with complex topologies. We use Self-Organizing Map neural networks whose neighborhood relationships are defined by a complex network, to classify handwritten digits. We show that topology has a small impact on performance and robustness to neuron failures, at least at long learning times. Performance may however be increased (by almost 10%) by evolutionary optimization of the network topology. In our experimental conditions, the evolved networks are more random than their parents, but display a more heterogeneous degree distribution. Categories and Subject Descriptors I.2.6 [Computing Methodologies]: ARTIFICIAL INTEL

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Fei Jiang, Hugues Berry, Marc Schoenauer
• Keywords: Artificial Intelligence, Complex Neural Networks, GECCO 2009, Map Neural Networks, Neural Networks