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Introduction In 1809, Carl Frederic Gauss [1] dealt in depth with problem of estimation of planetary and comet orbit parameters when the terrestrial observational data was sparse and imprecise. Gauss's problem bears strong resemblance to the problem we face in the estimation of neuronal state through electrical or optical measurements. Recent advances in Kalman filtering to estimate system state and parameters in nonlinear systems has offered the potential to apply such approaches to spatiotemporal neuronal systems. We here adapt the nonlinear method of unscented Kalman filtering (UKF) to observe the state and estimate parameters in a computational spatiotemporal excitable system that serves as a model of cerebral cortex. Methods We employ the modifications to the Wilson-Cowan equations as suggested by Pinto and Ermentrout [2], to model an experimental system in which we measure the wave dynamics of mammalian middle cortical layers using voltage sensitive dye. Employing a strategy of UKF suitable for neuronal models [3], we adapt covariance inflation from meteorological modeling to compensate for higher order errors known to occur in UKF [4].

Kalman filter control of a model of spatiotemporal cortical dynamics