****Integrated in the Area of Signal Processing and Data Analysis

Abstract: Understanding the relationship between network topology,
synchronization and information in active networks (e.g. neural
networks) is a fundamental matter to discover of the essence of
behavior, perception, and learning processes in animals and human
beings. In this talk I show that the study of idealistic autonomous
chaotic networks, formed by nodes that possess an equal dynamics, can
be used to understand a series of fundamental questions in
neurosciences. In particular, I will discuss two matters. How much
information from an arbitrary time-dependent stimulus that externally
excites a realistic active network can be realized by measuring the
time evolution of one of the nodes of the network and whether
synchronization is indeed important for the brain to perceive
objects. Contrary to the common sense, also network configurations for
which the nodes are highly desynchronous allow the transmission of
large amounts of information, and therefore, information does not
necessarily means synchronization.