Enhancing dynamical signatures of complex systems through symbolic computation
Alberto Porta, Mathias Baumert, Dirk Cysarz, Niels Wessel · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2014
The most pressing challenges that modern signal processing is facing are (i) to provide a description of the behaviour of complex systems formed by interacting subunits without modelling the functioning of single constituents and their relations, (ii) to establish fully multivariate frameworks for quantifying dependences among signals along predefined temporal directions, (iii) to disentangle dynamical signatures pertaining to the functioning of the system constituents, while preserving the possibility to describe the activity of the whole organism (i.e.coordinated behaviour), (iv) to interpret non-stationarities as a sequence of quasi-stationary states reflecting the determinism of the underlying system, (v) to extract and classify patterns associated to system conditions and specific behaviours, (vi) to be robust against noise affecting traces recorded in real experimental settings, and (vii) to be computationally efficient to promote applications in real time.Symbolic signal processing, defined as a set of modelfree techniques transforming a set of contemporaneously recorded signals (i.e. a multivariate recording) into sequences of symbols with the specific aim of enhancing distinct features while discarding unessential details, can naturally deal with all the above-mentioned challenges.Indeed, it is a data-driven approach focusing