Abstract
Background: Identification of dynamic and static characteristics of a given biological system promotes the understanding of the system behavior under a variety of circumstances. Purpose: To estimate the dynamic and static characteristics of a baroreflex neural arc from pressure input to efferent sympathetic nerve activity, we developed a system identification method using a neural cascade. Method: A "neuron" used in a neural network can represent the dynamic linear element followed by a nonlinear transfer function. By connecting two neurons in series, we can represent a system comprised of dynamic-linear (L1), static-nonlinear (NL), and dynamic-linear (L2) subsystems. Because the contamination of noise to the observed output resulted in biased estimates of the system characteristics, we added an iterative noise cancellation procedure where the noise was estimated by an autoregressive model. Results: In a simulation study, the neural cascade effectively identified the dynamic and static characteristics of an L1-NL-L2 system. The baroreflex neural arc is known to have derivative characteristics followed by a sigmoidal nonlinearity. When applied to the actual input-output data of the baroreflex neural arc obtained from rabbits, the neural cascade could identify the derivative characteristics followed by the sigmoidal nonlinearity. Conclusion: The neural cascade proposed in the present study may provide a useful method to simultaneously identify the dynamic and static characteristics of a biological system. [J Physiol Sci. 2006;56 Suppl:S73]
