Informational theories of consciousness: a review and extension.

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.

[1]  G. Tononi.  Consciousness as Integrated Information: a Provisional Manifesto , 2008, The Biological Bulletin.

[2]  David Gamez.  SpikeStream: A Fast and Flexible Simulator of Spiking Neural Networks , 2007, ICANN.

[3]  O. Sporns,et al.  Organization, development and function of complex brain networks , 2004, Trends in Cognitive Sciences.

[4]  Olaf Sporns,et al.  Measuring information integration , 2003, BMC Neuroscience.

[5]  Alexei V. Samsonovich.  Biologically inspired cognitive architectures : papers from the AAAI Fall Symposium , 2008 .

[6]  D. Chalmers.  Consciousness and Cognition , 1990 .

[7]  Giulio Tononi,et al.  Integrated Information in Discrete Dynamical Systems: Motivation and Theoretical Framework , 2008, PLoS Comput. Biol..

[8]  David Gamez.  The development and analysis of conscious machines , 2008 .

[9]  I Aleksander,et al.  Cyclic activity in nature. Causes of stability. , 1973, The International journal of neuroscience.

[10]  Olaf Sporns,et al.  Brain connectivity , 2007, Scholarpedia.

[11]  G. Edelman,et al.  Complexity and coherency: integrating information in the brain , 1998, Trends in Cognitive Sciences.

[12]  G. Edelman,et al.  A measure for brain complexity: relating functional segregation and integration in the nervous system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Anil K. Seth,et al.  Environment and Behavior Influence the Complexity of Evolved Neural Networks , 2004, Adapt. Behav..

[14]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[15]  David Gamez,et al.  Information integration based predictions about the conscious states of a spiking neural network , 2010, Consciousness and Cognition.

[16]  UnCheol Lee,et al.  Propofol induction reduces the capacity for neural information integration: Implications for the mechanism of consciousness and general anesthesia , 2008, Consciousness and Cognition.

[17]  S. Bressler,et al.  Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Anil K Seth,et al.  Theories and measures of consciousness: an extended framework. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Olaf Sporns,et al.  Evolving Coordinated Behavior by Maximizing Information Structure , 2006 .

[20]  George A. Mashour,et al.  Propofol Induction Reduces the Capacity for Neural Information Integration: Implications for the Mechanism of Consciousness and General Anesthesia , 2008 .

[21]  Igor Aleksander,et al.  Taking a Mental Stance Towards Artificial Systems , 2009, AAAI Fall Symposium: Biologically Inspired Cognitive Architectures.

[22]  Karl J. Friston,et al.  Dynamic causal modeling , 2010, Scholarpedia.

[23]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[24]  Olaf Sporns,et al.  Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.

[25]  M. Shanahan.  Dynamical complexity in small-world networks of spiking neurons. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  A. Seth.  Theories and measures of consciousness develop together , 2008, Consciousness and Cognition.