Information in Perception and Action

Since its inception for describing the laws of communication in the 1940's, information theory has been considered in fields beyond its original application area and, in particular, it was long attempted to utilize it for the description of intelligent agents. Already Attneave (1954) and Barlow (1961) suspected that neural information processing might follow principles of information theory and Laughlin (1998) demonstrated that information processing comes at a high metabolic cost; this implies that there would be evolutionary pressure pushing organismic information processing towards the optimal levels of data throughput predicted by information theory. This becomes particularly interesting when one considers the whole perception-action cycle, including feedback. In the last decade, significant progress has been made in this direction, linking information theory and control. The ensuing insights allow to address a large range of fundamental questions pertaining not only to the perception-action cycle, but to general issues of intelligence, and allow to solve classical problems of AI and machine learning in a novel way.

The workshop will present recent work on progress in AI, machine learning, control, as well as biologically plausible cognitive modeling, that is based on information theory.