On competition and learning in cortical structures
How do our brains make us live our lives? Despite the tremendous progress life sciences have made in the last decades we have no conclusive answer to this question. Today we are, however, able to build machines that outperform us in many ways. We can’t calculate as fast as our machines do, nor can we win against them when playing Backgammon, Chess, or Jeopardy. The paradigms upon which they function are fundamentally different from what we know about neural computation. Not only the computational substrate, but the very nature of human intelligence seems fundamentally different from the artificial intelligence (AI) we put into our machines. Existing computational models can be subdivided in two broad classes: (i) top-down models like the AI examples mentioned above that aim to reproduce high-level abilities, without worrying whether the model is in any way similar to the implementation in our brains, and (ii) bottom-up models that focus on using biologically plausible modules that are based on established neurophysiological and neuroanatomical knowledge. The ultimate goal is to understand the detailed neural correlates of cognition – a goal that is currently out of reach. In the first part of this thesis we use classical models for cortical competition to build adaptive systems that are capable of learning the relation between two input modalities. We go on to show that a similar system can be used to sharpen the connectivity in a network of interconnected areas.