 Models  Description 
1. 
Decoding movement trajectory from simulated grid cell population activity (Bush & Burgess 2019)



Matlab code to simulate a population of grid cells that exhibit both a rate and phase code for location in 1D or 2D environments, and are modulated by a human hippocampal LFP signal with highly variable frequency; then subsequently decode location, running speed, movement direction and an arbitrary fourth variable from population firing rates and phases in each oscillatory cycle. 
2. 
Hybrid oscillatory interference / continuous attractor NN of grid cell firing (Bush & Burgess 2014)



Matlab code to simulate a hybrid oscillatory interference  continuous attractor network model of grid cell firing in pyramidal and stellate cells of rodent medial entorhinal cortex 
3. 
Models of Vector Navigation with Grid Cells (Bush et al., 2015)



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 ratecoded grid cell representations before computing the displacement between them;
(2) The 'Ratecoded Vector Cell' model, which directly decodes the displacement between start and goal locations from ratecoded grid cell representations;
(3) The 'Phasecoded Vector Cell' model, which directly decodes the displacement between start and goal locations from the temporallycoded grid cell representations provided by phase precession;
(4) The 'Linear Lookahead' 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. 