Informational theories of consciousness: a review and extension.

Abstract

In recent years a number of people have suggested that there is a close link between conscious experience and the differentiation and integration of information in certain areas of the brain. The balance between differentiation and integration is often called information integration, and a number of algorithms for measuring it have been put forward, which can be used to make predictions about consciousness and to understand the relationships between neurons in a network. One of the key problems with the current information integration measures is that they take a lot of computer processing power, which limits their application to networks of around a dozen neurons. There are also more general issues about whether the current algorithms accurately reflect the consciousness associated with a system. This paper addresses these issues by exploring a new automata-based algorithm for the calculation of information integration. To benchmark different approaches we implemented the Balduzzi and Tononi algorithm as a plugin to the SpikeStream neural simulator, and used it to carry out some preliminary comparisons of the liveliness and Φ measures on simple four neuron networks.