Guided self-organization of input-driven recurrent neural networks

To understand the world around us, our brains solve a variety of tasks. One of the crucial functions of a brain is to make predictions of what will happen next, or in the near future. This ability helps us to anticipate upcoming events and plan our reactions to them in advance. To make these predictions, past information needs to be stored, transformed or used otherwise. How exactly the brain achieves this information processing is far from clear and under heavy investigation. To guide this extraordinary research effort, neuroscientists increasingly look for theoretical frameworks that could help explain the data recorded from the brain, and to make the enormous task more manageable. This is evident, for instance, through the funding of the billion-dollar "Human Brain Project", of the European Union, amongst others. Mathematical techniques from graph and information theory, control theory, dynamical and complex systems (Sporns, 2011), statistical mechanics (Rolls and Deco, 2010), as well as machine learning and computer vision (Seung, 2012; Hawkins and Blakeslee, 2004), have provided new insights into brain structure and possible function, and continue to generate new hypotheses for future research.