A systems theoretic approach to the study of CNS function

This paper presents a paradigm for using a general systems theoretic approach to study central nervous system function. Neuronal systems are conceptualized as consisting of different populations of neurons, each of which may be treated as a unit with its own dynamic properties. These dynamic properties of each population are determined by fundamental characteristics of its constituent neurons. Different populations interact with each other through the network structure in which they are embedded. We are modeling the linear and nonlinear properties of these systems using a theoretical framework based on two complementary approaches. First, a functional power series is used to characterize the input/output properties of the system. Second, a state-variable approach is used to characterize the internal structure and function of the system and to identify specific relationships among physiological variables. We have extensively investigated the functional power series approach to study the cat somatosensory system and the rabbit hippocampal formation. Both of these systems exhibit nonlinear properties in their response to electrical stimulation, and these nonlinear properties are characterized by the high-order kernels of the functional power series. State-variable models are being formulated to map these input/output properties onto internal models of the systems.