A unifying computational framework for stability and flexibility of arousal.

ABSTRACT: Arousal and consciousness flexibly adjust to salient cues, but remain stable despite noise and disturbance. Diverse, highly interconnected neural networks govern the underlying transitions of behavioral state; these networks are robust but very complex. Frameworks from systems engineering provide powerful tools for understanding functional logic behind component complexity. From a general systems viewpoint, a minimum of three communicating control modules may enable flexibility and stability to coexist. Comparators would subtract current arousal from desired arousal, producing an error signal. Regulators would compute control signals from this error. Generators would convert control signals into arousal, which is fed back to comparators, to make the system noise-proof through self-correcti

SUBMITTER: Kosse C 

PROVIDER: S-EPMC4202806 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature