Figure 1. Multiple mechanisms for the implementation of path integration.

(A) Path integration mechanisms for mammalian species such as rodents, who forage under conditions with sparse visual and landmark cues, have been proposed to predominantly rely on internally generated estimates of speed and direction. The internal direction and distance estimates are integrated by grid cells, which could provide a path-integration based estimate of the current position. Here, direction signals are assumed to be provided by internally generated head direction cells, and distance is computed using internally generated speed signals that also manifest within the brain. (B) One major class of grid cell generation models are the oscillatory interference models [5,7]. In this class of models, signals are integrated by velocity controlled oscillators that receive head direction and speed inputs. Grid cells receive information from multiple oscillators that each respond to a different direction. Coincident peak phases of activity result in grid cell firing, whereas cells remain silent when the oscillators are out of phase with each other. Adapted from [4,7]. (C) The other major class of grid cell generation models are continuous attractor network models, which generate grid patterns from local recurrent network connectivity. Depicted here is one such implementation [4]. Excitatory and inhibitory connections between cells lead to a ‘bump’ of focused activity in the network, which is gradually moved in the cell sheet by directional and speed signals. Adapted from [4,7].