The goals of this proposal are to further develop and apply a combined theoretical and experimental approach, utilizing principles of nonlinear systems theory, for achieving a biologically-based model of the hippocampal formation. In this approach, the functional properties resulting from interaction among the elements of a neural network are quantitatively characterized as input/output functions, i.e., the kernels of a functional power series. The linear and higher-order nonlinear components of the input/output relationship are determined experimentally by stimulating afferents to the network with random inputs to generate a wide range of interactions among the network elements, simultaneously recording activity of the output neurons, and estimating the kernels using cross-correlation or other techniques. The hippocampal formation consists of five subsystems (entorhinal cortex, dentate gyrus, the CA3/4 and CA1/2 pyramidal cell regions of Ammons' horn, and the subicular cortex) interconnected through feedforward and feedback pathways. Studies will focus on entorhinal input to the dentate gyrus, and will extend to CA3/4. Entorhinal afferents to the dentate will be activated with a train of electrical impulses having randomly determined (Poisson) inter-impulse intervals; evoked responses will be recorded electrophysiologically from dentate granule cells. Cross-correlation and a novel Laguerre expansion techniques will be used to estimate the kernels. The identical procedures will be repeated for progressively simplified in vivo and in vitro preparations in which the dentate (and later, other subsystems) are isolated from the remaining network granule cells isolated from intrinsic interneurons, and ultimately, feedback mechanisms intrinsic to granule cells (e.g., voltage-dependent conductances) isolated from the synaptic currents generated in response to the randomized input. In this manner, the biological mechanisms responsible for the nonlinearities expressed by the intact system can be identified. Models of single cells and circuits characterized experimentally will be developed using multi-dimensional Laplace transforms, allowing a progressively more complex representation of the global hippocampal system as a composite of the input/output functions of its subsystems. Our ultimate objective is to utilize such a model is to identify the functional dynamics of the hippocampus expressed at a systems level, and to investigate the relationship between those dynamics and learning-related changes in hippocampal activity recorded in behaving animals and humans.
