Reduction of visual information in neural network learning process visualization

Visualization of the learning of neural network faces the problem of dealing with overwhelming amount of visual information. This paper describes the application of clustering methods for reduction of visual information in the response function visualization. When only clusters of neurons are visualized, instead of direct visualization of responses of all neurons in the network, the amount of visually presented information can be significantly reduced. This is useful for reducing user fatigue and also for minimizing the visualization equipment requirements. We show, that application of Kohonen network or growing neural gas with utility factor algorithm allows to visualize the learning of moderate-sized neural networks in real time. Comparison of both algorithms in this task is provided, also with performance analysis and example results of response function visualization.