The estimation of long-term memory characteristics in MEA neuronal culture recordings

The nonlinear analysis of multichannel MEA recordings from neuronal networks is becoming a central topic in Neuroengineering. Up-to-date these kind of analyses required complex ad hoc methods. In this paper we introduce a new approach that allows the analysis of the whole-neuronal-network-activity with well-established nonlinear signal processing methods. In particular, we show here the estimation of long-term-memory behaviors through the Periodogram method in the bursting activity of cortical neuron cultures during development. Moreover, we show how this method is able to highlight structural activity changes of the network.