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.
Esposti
Federico
F
Politecnico di Milano technical University, Milan, Italy. federico.esposti@polimi.it
Signorini
Maria Gabriella
MG
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001683:Biological Clocks / Q000502:physiology*; D002460:Cell Line; D003198:Computer Simulation; D017774:Long-Term Potentiation / Q000502:physiology*; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D051381:Rats