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Data assimilation is naturally conceived as the synchronization of two systems, “truth” and “model”, coupled through a limited exchange of information (observed data) in one direction. Though investigated most thoroughly in meteorology, the task of data assimilation arises in any situation where a predictive computational model is updated in run time by new observations of the target system, including the case where that model is a perceiving biological mind. In accordance with a view of a semi-autonomous mind evolving in synchrony with the material world, but not slaved to it, the goal is to prescribe a coupling between truth and model for maximal synchronization. It is shown that optimization leads to the usual algorithms for assimilation via Kalman Filtering under a weak linearity assumption. For nonlinear systems with model error and sampling error, the synchronization view gives a recipe for calculating covariance inflation factors that are usually introduced on an ad hoc basis. Consciousness can be framed as self-perception, and represented as a collection of models that assimilate data from one another and collectively synchronize. The combination of internal and external synchronization is examined in an array of models of spiking neurons, coupled to each other and to a stimulus, so as to segment a visual field. The inter-neuron coupling appears to enhance the overall synchronization of the model with reality.

DOI: 10.1007/978-3-642-33359-0_8