grid_cell is a computational model including:

• mathematically modelled entorhinal grid cells which have multiple firing fields arranged regularly in hexagonal, grid like pattern
• implementation of phase precession in spike phases relative to hippocampal theta oscillations
• a perceptron-based decoder to compare encoding speeds of rate code, phase code and complex(rate-phase) code
• integration of these to a biophysically realistic model of dentate gyrus, pyDentate, to investigate pattern separation on types of grid cell population code

If you have questions feel free to contact me ([email protected])

Grid cell model: Solstad, T., Moser, E. I., & Einevoll, G. T. (2006). From Grid Cells to Place Cells : A Mathematical Model. Hippocampus, 1031, 1026–1031. https://doi.org/10.1002/hipo

Phase precession implemention adapted for our grid model: Bush, D., & Burgess, N. (2020). Advantages and detection of phase coding in the absence of rhythmicity. Hippocampus, 30(7), 745–762. https://doi.org/10.1002/hipo.23199

Perceptron approach adopted from: Cayco-Gajic, N. A., Clopath, C., & Silver, R. A. (2017). Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks. Nature Communications, 8(1), 1–11. https://doi.org/10.1038/s41467-017-01109-y

pyDentate model: Braganza, O., Müller-Komorowska, D., Kelly, T., & Beck, H. (2019). Quantitative properties of a feedback circuit predict frequency-dependent pattern separation. ELife. https://doi.org/10.1101/813188

Barış Can Kuru - Institute of Experimental Epileptology and Cognition Research