Welcome to the
Nonlinear Systems & Electronics Lab
Here you can find info on what research topics we pursue, what results we have already published, and other interesting stuff!
Chasing chaos
Detecting chaos in experiments is challenging. If analytical tools are used carelessly, unreasonable results are guaranteed. In the NSE Lab we investigate new approaches in nonlinear time series analysis to tackle this crucial issue. During the years we have contributed to the field with several interesting works...
Exploring brain networks
It is well known that any human brain organizes its activity by means of networks comprising several thousands of neurons. However, the properties and dynamics of brain networks is not fully understood yet. In the NSE Lab we use a newly devised algorithm to study brain connectivity probed via magnetoencephalography. The algorithm has already revealed some interesting features concerning link lengths and the corresponding time scales of observed connectivity...
Mathematical physics of entropy
Characterization of signals produced by complex systems relies on the information-theoretical notion of entropy. In the NSE Lab we investigate the properties and applicability of entropy-like measures, such as permutation entropy, in the field of nonlinear dynamics. In this context, a key role is played by Markov chains and the corresponding central limit theorem, a crucial tool for which we provided a novel proof...
Surrogate spike sequences
Many dynamical systems generate signals that are sequences of discrete events, such as neuron spikes or tornadoes. Standard statistical techniques are often unsuitable to deal with this kind of system: a possible approach relies on the generation of "surrogate" datasets that share statistical properties with the original one. We developed a computationally efficient and robust method for the generation of surrogate event sequences. Enter JODI...
Unraveling micro-RNA
Many scientific works regard the possible use of miRNA levels as diagnostic and prognostic tools for cancer. Developing reliable classifiers requires tackling several crucial aspects, some of which have been widely overlooked in the scientific literature. We developed a new Bayesian classifier to be used in this challenging field, and we already demonstrated its reliability and usefulness in several works...
