Four models of vector navigation in large scale 2D space using grid cell representations of location are included: (1) The 'Distance Cell' model, which directly decodes absolute start and goal locations in allocentric space from rate-coded grid cell representations before computing the displacement between them; (2) The 'Rate-coded Vector Cell' model, which directly decodes the displacement between start and goal locations from rate-coded grid cell representations; (3) The 'Phase-coded Vector Cell' model, which directly decodes the displacement between start and goal locations from the temporally-coded grid cell representations provided by phase precession; (4) The 'Linear Look-ahead' model, which uses a directed search through grid cell representations, initiated at the start location and then moving along a specific axis at a constant speed, to compute the displacement between start and goal locations.

Model Type: Realistic Network

Region(s) or Organism(s): Entorhinal cortex

Model Concept(s): Spatial Navigation; Grid cell

Simulation Environment: MATLAB

Implementer(s): Bush, Daniel [drdanielbush @ gmail.com]

References:

Bush D, Barry C, Manson D, Burgess N. (2015). Using Grid Cells for Navigation. Neuron. 87 [PubMed]