Complexity in Neural and Financial Systems: From Time-Series to Networks

When can a system be unambiguously defined as "complex"? Although many real-world systems are believed to bear its signature, the question above remains unanswered. Our special issue aims at contributing to this ongoing discussion by collecting a number of studies tackling two aspects of complexity that have recently gained increasing attention: the temporal one and the structural one. The seven papers composing this special issue offer an articulated overview of these topics, by proposing novel techniques for the analysis of systems described by multiple time series (such as functional brain data, stock prices and market indices) and networked interaction patterns. The choice of focusing on neural and financial systems is dictated by the importance that topics like the identification of precursors of stock market movements, the application of causality-testing techniques to brain data and the definition of null models for the analysis of correlation matrices (only to mention a few) have gained in recent years.