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Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis
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
doi:10.7554/elife.10094
pmid:26952211
pmcid:PMC4841785
fatcat:lrxpqe4oevd7lihb6i2f32q4re