Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis

Yedidyah Dordek, Daniel Soudry, Ron Meir, Dori Derdikman
2016 eLife  
Many recent models study the downstream projection from grid cells to place cells, while recent data have pointed out the importance of the feedback projection. We thus asked how grid cells are affected by the nature of the input from the place cells. We propose a single-layer neural network with feedforward weights connecting place-like input cells to grid cell outputs. Place-to-grid weights are learned via a generalized Hebbian rule. The architecture of this network highly resembles neural
more » ... works used to perform Principal Component Analysis (PCA). Both numerical results and analytic considerations indicate that if the components of the feedforward neural network are non-negative, the output converges to a hexagonal lattice. Without the non-negativity constraint, the output converges to a square lattice. Consistent with experiments, grid spacing ratio between the first two consecutive modules is −1.4. Our results express a possible linkage between place cell to grid cell interactions and PCA.
doi:10.7554/elife.10094 pmid:26952211 pmcid:PMC4841785 fatcat:lrxpqe4oevd7lihb6i2f32q4re