46 citations · 225 across the 28 of their papers we have counts for
28 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…
Symbolic analysis of bursting dynamical regimes of Rulkov neural networks
R. C. Budzinski, S. R. Lopes, C. Masoller
Neurons modeled by the Rulkov map display a variety of dynamic regimes that include tonic spikes and chaotic bursting. Here we study an ensemble of bursting neurons coupled with th…
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…