1 citations · 1 across the 1 of their papers we have counts for
5 papers
Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks
B. R. R. Boaretto, R. C. Budzinski, K. L. Rossi +3
Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are…
Parameter free determination of optimum time delay
Thiago Lima Prado, Vandertone Santos Machado, Gilberto Corso +2
We show that the same maximum entropy principle applied to recurrence microstates configures a new way to properly compute the time delay necessary to correctly sample a data set.…
Synchronization malleability in neural networks under a distance-dependent coupling
R. C. Budzinski, K. L. Rossi, B. R. R. Boaretto +2
We investigate the synchronization features of a network of spiking neurons under a distance-dependent coupling following a power-law model. The interplay between topology and coup…
Effects of neuronal variability on phase synchronization of neural networks
Kalel Luiz Rossi, Roberto Cesar Budzisnki, Joao Antonio Paludo Silveira +4
An important idea in neural information processing is the communication-through-coherence hypothesis, according to which communication between two brain regions is effective only i…
Parameter-free quantification of stochastic and chaotic signals
Sergio Roberto Lopes, Thiago de Lima Prado, Gilberto Corso +2
Recurrence entropy is a novel time series complexity quantifier based on recurrence microstates. Here we show that is a \textit{parameter-free} qu…