Complex and Adaptive Dynamical Systems: A Primer

We are living in an ever more complex world, an epoch where human actions can accordingly acquire far-reaching potentialities. Complex and adaptive dynamical systems are ubiquitous in the world surrounding us and require us to adapt to new realities and the way of dealing with them. This primer has been developed with the aim of conveying a wide range of "commons-sense" knowledge in the field of quantitative complex system science at an introductory level, providing an entry point to this both fascinating and vitally important subject. The approach is modular and phenomenology driven. Examples of emerging phenomena of generic importance treated in this book are: -The small world phenomenon in social and scale-free networks; -Phase transitions and self-organized criticality in adaptive systems; -Life at the edge of chaos and coevolutionary avalanches resulting from the unfolding of all living; -The concept of living dynamical systems and emotional diffusive control within cognitive system theory. Technical course prerequisites are a basic knowledge of ordinary and partial differential equations and of statistics. Each chapter comes with exercises and suggestions for further reading - solutions to the exercises are also provided.

[1]  Kenji Doya,et al.  What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? , 1999, Neural Networks.

[2]  G. Carpenter.  Neural-network models of learning and memory: leading questions and an emerging framework , 2001, Trends in Cognitive Sciences.

[3]  S. Dehaene,et al.  Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework , 2001, Cognition.

[4]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[5]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[6]  C. Koch,et al.  A framework for consciousness , 2003, Nature Neuroscience.

[7]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[8]  Naftali Tishby,et al.  Cortical activity flips among quasi-stationary states. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[9]  J. Elman.  An alternative view of the mental lexicon , 2004, Trends in Cognitive Sciences.

[10]  Isaac Meilijson,et al.  Effective Neuronal Learning with Ineffective Hebbian Learning Rules , 2001, Neural Computation.

[11]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[12]  Edmund T. Rolls,et al.  Introduction to Connectionist Modelling of Cognitive Processes , 1998 .

[13]  R. O’Reilly,et al.  Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain , 2000 .

[14]  A. Selverston,et al.  Dynamical principles in neuroscience , 2006 .

[15]  Dana H. Ballard,et al.  An introduction to natural computation , 2000, Complex adaptive systems.

[16]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[17]  P. Bak,et al.  Learning from mistakes , 1997, Neuroscience.

[18]  A. Grinvald,et al.  Spontaneously emerging cortical representations of visual attributes , 2003, Nature.

[19]  Claudius Gros,et al.  Self-sustained Thought Processes in a Dense Associative Network , 2005, KI.

[20]  B. Baars,et al.  How conscious experience and working memory interact , 2003, Trends in Cognitive Sciences.