Place and grid cells in a loop (Rennó-Costa & Tort 2017)

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This model implements a loop circuit between place and grid cells. The model was used to explain place cell remapping and grid cell realignment. Grid cell model as a continuous attractor network. Place cells have recurrent attractor network. Rate models implemented with E%-MAX winner-take-all network dynamics, with gamma cycle time-step.
1 . Rennó-Costa C, Tort ABL (2017) Place and Grid Cells in a Loop: Implications for Memory Function and Spatial Coding. J Neurosci 37:8062-8076 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Hippocampus; Entorhinal cortex;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Gap Junctions:
Simulation Environment: Python;
Model Concept(s): Gamma oscillations; Rate-coding model neurons; Winner-take-all; Place cell/field; Pattern Separation; Synaptic Plasticity;
Implementer(s): Rennó-Costa, César [rennocosta at];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell;
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