Towards a New Information Processing Measure for Neural Computation

Abstract

The understanding of the relation between structure and function in the brain requires theoretical frameworks capable of dealing with a large variety of complex experimental data. Likewise neural computation strives to design structures from which complex functionality should emerge. The framework of information theory has been partially successful in explaining certain brain structures with respect to sensory transformations under restricted conditions. Yet classical measures of information have not taken an explicit account of some of the fundamental concepts in brain theory and neural computation: namely that optimal coding depends on the specific task(s) to be solved by the system, and that autonomy and goal orientedness also depend on extracting relevant information from the environment and specific knowledge from the receiver to be able to affect it in the desired way. This paper presents a general (i.e. implementation independent) new information processing measure that takes into account the previously mentioned issues. It is based on measuring the transformations required to go from the original alphabet in which the sensory messages are represented, to the objective alphabet which depends on the implicit task(s) imposed by the environment-system relation.