Abstract
Dynamics of the activity of neuronal networks have been intensively studied from the view point of the nonlinear dynamical system. The neuronal activities are recorded as multivariate time series of the epochs of spike occurrences–the spike trains–which are often effected by intrinsic and measuring noise. The spike trains can be considered as a mixture of a realization of deterministic and stochastic processes. Within this framework we considered several simulated spike trains derived from the Zaslavskii map with additive noise. The ensemble of all preferred firing sequences detected by the pattern grouping algorithm (PGA) in the noisy spike trains form a new time series that retains the dynamics of the original mapping.
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© 2006 Springer-Verlag Berlin Heidelberg
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Asai, Y., Yokoi, T., Villa, A.E.P. (2006). Detection of a Dynamical System Attractor from Spike Train Analysis. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840817_65
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DOI: https://doi.org/10.1007/11840817_65
Publisher Name: Springer, Berlin, Heidelberg
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