The brain is without doubt the most complex adaptive system known to humanity, arguably also a complex system about which we know very little. Throughout this book we have considered and developed general guiding principles for the understanding of complex networks and their dynamical properties; principles and concepts transcending the details of specific layouts realized in real-world complex systems. We follow the same approach here, considering the brain as just one example of what is called a cognitive system, a specific instance of what one denotes, cum grano salis, a living dynamical system. In the first part we will treat general layout considerations concerning dynamical organizational principles, an example being the role of diffuse controlling and homeostasis for stable long-term cognitive information processing. Special emphasis will be given to the motivational problem – how the cognitive system decides what to do – in terms of survival parameters of the living dynamical system and the so-called emotional diffusive control. In the second part we will discuss two specific generalized neural networks implementing various aspects of these general principles: a dense and homogeneous associative network (dHAN) for environmental data representation and associative thought processes, and the simple recurrent network (SRN) for concept extraction from universal prediction tasks.
CITATION STYLE
Gros, C. (2013). Elements of Cognitive Systems Theory. In Complex and Adaptive Dynamical Systems (pp. 257–297). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-36586-7_8
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