Navigation requires integration of external and internal inputs to form a representation of location. Part of this integration is considered to be carried out by the grid cells network in the medial entorhinal cortex (MEC). However, the structure of this neural network is unknown. To shed light on this structure, we measured noise correlations between 508 pairs of simultaneous previously recorded grid cells. We differentiated between pure grid and conjunctive cells (pure grid in Layers II, III, and VI vs. conjunctive in Layers III and V—only Layer III was bi-modal), and devised a new method to classify cell pairs as belonging/not-belonging to the same module. We found that pairs from the same module show significantly more correlations than pairs from different modules. The correlations between pure grid cells decreased in strength as their relative spatial phase increased. However, correlations were mostly at 0 time-lag, suggesting that the source of correlations was not only synaptic, but rather resulted mostly from common input. Given our measured correlations, the two functional groups of grid cells (pure vs. conjunctive), and the known disorganized recurrent connections within Layer II, we propose the following model: conjunctive cells in deep layers form an attractor network whose activity is governed by velocity-controlled signals. A second manifold in Layer II receives organized feedforward projections from the deep layers, giving rise to pure grid cells. Numerical simulations indicate that organized projections induce such correlations as we measure in superficial layers. Our results provide new evidence for the functional anatomy of the entorhinal circuit—suggesting that strong phase-organized feedforward projections support grid fields in the superficial layers. © 2015 Wiley Periodicals, Inc.