ABSTRACT: Recordings in awake, behaving animals demonstrate that cells in medial entorhinal cortex (mEC) show ‘‘grid cell’ ’ firing activity when a rat explores an open environment. Intracellular recording in slices from different positions along the dorsal to ventral axis show differences in intrinsic properties such as subthreshold membrane potential oscillations (MPO), resonant frequency, and the presence of the hyperpolarization-activated cation current (h-current). The differences in intrinsic properties correlate with differences in grid cell spatial scale along the dorsal–ventral axis of mEC. Two sets of computational models have been proposed to explain the grid cell firing phenomena: oscillatory interference models and attractor-dynamic models. Both types of computational models are briefly reviewed, and cellular experimental evidence is interpreted and presented in the context of both models. The oscillatory interference model has variations that include an additive model and a multiplicative model. Experimental data on the voltage-dependence of oscillations presented here support the additive model. The additive model also simulates data from ventral neurons showing large spacing between grid firing fields within the limits of observed MPO frequencies. The interactions of h-current with synaptic modification suggest that the difference in intrinsic properties could also contribute to differences in grid cell properties due to attractor dynamics along the dorsal to ventral axis of mEC. Mechanisms of oscillatory interference and attractor dynamics may make complementary contributions to the properties of grid cell firing in entorhinal cortex. VC 2008 Wiley-Liss, Inc. KEY WORDS: grid cells; entorhinal cortex; stellate cell; membrane oscillations; computational models

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Quantitative models of human memory often rely on assumed latent memory processes, such as patterns of rehearsal of the words on a study list. Consequently, the application of memory models that assume latent rehearsals typically make use of overt rehearsal data. However, these data are not always available in some settings where the application of memory models has proven useful (e.g., clinical assessments of memory performance). In this paper, we show Bayesian statistical methodology can be used to infer the latent pattern of rehearsals needed to successfully apply a temporal model of memory to a clinical data set. We discuss the relevance of this research for those interested in neuropsychological assessment as well as cognitive psychologists interested in basic memory research. Keywords: Alzheimer’s disease and related disorders; Cognitive psychometrics; Hierarchical Bayesian modeling; Human memory; Missing data