Abstract
The Multi-electrode Array (MEA) technology allows the in-vitro culture of neuronal networks that can be used as a simplified and accessible model of the central nervous system, given that they exhibit activity patterns similar to the in-vivo tissue. Current devices generate huge amounts of data, thus motivating the development of systems capable of discriminating diverse cultured neuronal network activity patterns. In this paper, we describe the use of Inter-Spike Interval statistics coupled to data compression statistics in two discrimination applications. One of them concerning spontaneous vs. stimulated activity patterns, and the other concerning spontaneous responses from a control culture of neurons and a previously treated one. We show that the data compression ratio of the trains of spikes emerging from those cultures can be used to enhance the discrimination performance.
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Perez-Uribe, A., Satizábal, H.F. (2012). Artificial Neural Networks and Data Compression Statistics for the Discrimination of Cultured Neuronal Activity. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_26
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DOI: https://doi.org/10.1007/978-3-642-33269-2_26
Publisher Name: Springer, Berlin, Heidelberg
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