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Models of Vector Navigation with Grid Cells (Bush et al., 2015)
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Model Information
Model File
Accession:
182685
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.
Reference:
1 .
Bush D, Barry C, Manson D, Burgess N (2015) Using Grid Cells for Navigation
Neuron
87
:507-520
Citations
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Model Information
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Model Type:
Realistic Network;
Brain Region(s)/Organism:
Entorhinal cortex;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment:
MATLAB;
Model Concept(s):
Spatial Navigation;
Grid cell;
Implementer(s):
Bush, Daniel [drdanielbush @ gmail.com];
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BushEtAl2015
Readme.pdf
readme.txt
DistanceCellModel.m
LinearLookAheadModel.m
PhaseModel.m
VectorCellModel.m
Please see the Readme.pdf for detailed instructions on how to run the model.