Abstract
The present paper follows the initial work on multidimensional static data clustering using a novel neural network structure, namely Echo state network (ESN). Here we exploit dynamic nature of these networks for solving of clustering task of a multidimensional dynamic data set. The data used in this investigation are taken from an experimental set-up applied for tasting of visual discrimination of complex dot motions. The proposed here model, although far from complicated brain theories, can serve as a good basis for investigation of humans perception.
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Koprinkova-Hristova, P., Alexiev, K. (2013). Echo State Networks in Dynamic Data Clustering. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_43
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DOI: https://doi.org/10.1007/978-3-642-40728-4_43
Publisher Name: Springer, Berlin, Heidelberg
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