Behaving systems, biological as well as artificial, need to respond quickly and accurately to changes in the environment. A highly nonlinear system dynamics is required in order to cope with a complex and changing environment, and this dynamics should be regulated to match the demands of the current situation, and to predict future behavior. If any of these regulatory systems fail, the balance between order and disorder can be shifted, resulting in an inappropriate and unpredictable behaviour.

This presentation addresses the issue of stability and flexibility of neural systems, and how a balance can be achieved. Assuming a close correspondence with cognitive and mental processes, we use cortical neural network models to investigate how regulation of the neurodynamics can result in an efficient information processing, in terms of attention, learning and associative memory.

In particular, we use our models to investigate relations between structure, dynamics and function of various neural systems (at different spatial and temporal scales), and how the stability-flexibility dilemma may be solved by proper regulation. We focus on the complex neurodynamics and its modulation, and how this is related to the neural circuitry, where neuronal excitability and synaptic modification are considered. Finally, we discuss the relevance of these results to clinical and experimental neuroscience and speculate on a link between neural instability and mental disorders.