Neural Network Model for Blood Pressure Regulation
Author Information
Author(s): Silvano B. Zanutto, Bruno Cernuschi FrÃas, Max E. Valentinuzzi
Primary Institution: Universidad de Buenos Aires
Hypothesis
The sympathetic nervous control of blood pressure is developed through a neural network model that learns from chemoreceptor feedback.
Conclusion
The model successfully simulates how the sympathetic nervous system regulates blood pressure by adapting to feedback from cardiovascular receptors.
Supporting Evidence
- The model shows that sympathetic efferent discharge rates can be adjusted to maintain normal oxygen and carbon dioxide levels.
- Learning in the model is completed when chemoreceptor output reaches zero, indicating optimal regulation.
- The model suggests that the NTS acts as a comparator in cardiovascular regulation.
Takeaway
This study created a computer model to show how our body learns to control blood pressure using signals from sensors that detect oxygen and carbon dioxide levels.
Methodology
A computer iterative model simulating the development of sympathetic nervous control based on feedback from chemoreceptors.
Limitations
The model only simulates sympathetic development and does not include other regulatory mechanisms.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website