Abstract
Affective disorders tend to be recurrent and progressive and illness patterns typically evolve from isolated episodes at the beginning to more rapid, rhythmic and finally irregular "chaotic" mood patterns. Chararacteristic timecourse and disease patterns have prompted the consideration of nonlinear dynamics and chaos. In this paper we review some of our recent work where we addressed the relevance of nonlinear, stochastic and resulting cooperative dynamics for disease patterns and temporal course of affective disorders by use of a computational approach.
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