****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.