Adaptive neural network that subserves optimal homeostatic control of breathing.
An adaptive neural network model that exhibits the optimality and homeostasis characteristics of the respiratory control system is described. Based upon the Hopfield network structure and a postulated Hebb-like respiratory synapse with correlational short-term potentiation, the model is capable of mimicking the normal ventilatory responses to exercise and CO2 inputs without the need for an explicit exercise stimulus. Results suggest the possibility of an adaptive neuronal mechanism that effects optimal homeostatic regulation of respiration in mammals.
Poon
C S
CS
Harvard-MIT Division of Health Sciences and Technology, Cambridge 02139.
D000222:Adaptation, Physiological / Q000502:physiology*; D000818:Animals; D003198:Computer Simulation; D015444:Exercise / Q000502:physiology; D006706:Homeostasis / Q000502:physiology; D008322:Mammals; D008954:Models, Biological; D016571:Neural Networks, Computer; D012016:Reference Values; D012119:Respiration / Q000502:physiology*