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.
Model Type: Connectionist Network; Neuron or other electrically excitable cell
Region(s) or Organism(s): Hippocampus; Entorhinal cortex
Cell Type(s): Hippocampus CA1 pyramidal GLU cell
Model Concept(s): Gamma oscillations; Rate-coding model neurons; Winner-take-all; Place cell/field; Pattern Separation; Synaptic Plasticity
Simulation Environment: Python
Implementer(s): Rennó-Costa, César [rennocosta at neuro.ufrn.br]
References:
Rennó-Costa C, Tort ABL. (2017). Place and Grid Cells in a Loop: Implications for Memory Function and Spatial Coding. The Journal of neuroscience : the official journal of the Society for Neuroscience. 37 [PubMed]