%0 Journal Article
%T Quantitative Estimation of the Nonstationary Behavior of Neural Spontaneous Activity
%A Jo£¿o-Batista Destro-Filho
%A Carlos-Alberto Estombelo-Montesco
%A Luiz-Otavio Murta-Junior
%A Sergio Martinoia
%A Michela Chiappalone
%A Suelen Moreira-Marques
%A Amanda F. Neves
%J Computational Intelligence and Neuroscience
%D 2010
%I Hindawi Publishing Corporation
%R 10.1155/2010/785919
%X The ¡°stationarity time¡± (ST) of neuronal spontaneous activity signals of rat embryonic cortical cells, measured by means of a planar Multielectrode Array (MEA), was estimated based on the ¡°Detrended Fluctuation Analysis¡± (DFA). The ST is defined as the mean time interval during which the signal under analysis keeps its statistical characteristics constant. An upgrade on the DFA method is proposed, leading to a more accurate procedure. Strong statistical correlation between the ST, estimated from the Absolute Amplitude of Neural Spontaneous Activity (AANSA) signals and the Mean Interburst Interval (MIB), calculated by classical spike sorting methods applied to the interspike interval time series, was obtained. In consequence, the MIB may be estimated by means of the ST, which further includes relevant biological information arising from basal activity. The results point out that the average ST of MEA signals lies between 2-3 seconds. Furthermore, it was shown that a neural culture presents signals that lead to different statistical behaviors, depending on the relative geometric position of each electrode and the cells. Such behaviors may disclose physiological phenomena, which are possibly associated with different adaptation/facilitation mechanisms. 1. Introduction The digital processing of biological signals may be considered a challenging task [1], due to the underlying characteristics of such systems and signals: the nonlinearity, which is closely connected with the complex behavior of the alive organisms [2, 3]; and the nonstationarity of the time series [4]. A classical mathematical procedure in neuronal signal processing consists of the detection of spikes connected with action potentials, which requires the establishment of an amplitude threshold, above which any potential is considered a spike [5]. The next step is devoted to the estimation of the ¡°Interspike Interval¡± (ISI) time series, including spike classification [6], which enables several analyses in the field of neuronal coding [3]. Notice that spike classification is based on pattern recognition theory, involving tools such as Mahalanobis minimum distance [6, 7] and Independent Component Analysis [5]. In addition, neural connectivity [8] is also a very important research field, based on the application of cross-correlation theory [9¨C12] and spectral coherence [13] to the ISI time series, in order to evaluate the network of synaptic connections among cells within the cultured tissue. All these signal processing techniques are based on the concept of ¡°ISI time series¡± [8], the estimation
%U http://www.hindawi.com/journals/cin/2010/785919/