| | 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 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. |
