Neural stability and flexibility: a computational approach.

This paper 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 a cortical neural network model to investigate how regulation of the neurodynamics can result in an efficient information processing, in terms of learning and associative memory. In particular, we use this model to investigate relations between structure, dynamics, and function of a neural system, 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 synaptic modification and network pruning 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.