Many species of mammals can keep track of spatial location even in the absence of visual, auditory, olfactory, or tactile cues, by integrating their movements—the ability to do this is referred to in the literature as path integration. A number of theoretical models have explored mechanisms by which path integration could be performed by neural networks. In most models, such as those of Samsonovich and McNaughton (1997) or Burak and Fiete (2009), the principal ingredients are (1) an internal representation of position, (2) internal representations of the speed and direction of movement, and (3) a mechanism for shifting the encoded position by the right amount when the animal moves. Because cells in the MEC encode information about position (grid cells) and movement (head direction cells and conjunctive position-by-direction cells), this area is currently viewed as the most promising candidate for the place in the brain where path integration occurs. However, the question remains unresolved, as in humans the entorhinal cortex does not appear to be required for path integration. Burak and Fiete (2009) showed that a computational simulation of the grid cell system was capable of performing path integration to a high level of accuracy. However, more recent theoretical work has suggested that grid cells might perform a more general denoising process not necessarily related to spatial processing.