Many recent models study the downstream projection from grid cells to place cells, while recent data has 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 were learned via a generalized Hebbian rule. The architecture of this network highly resembles neural networks used to perform Principal Component Analysis (PCA).
Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis, Y. Dordek, D. Soudry, R. Meir, D. Derdikman, Elife 2016
A Tractable approximation to optimal point process filtering: application to neural encoding, Y. Harel, R. Meir, M. Opper, NIPS 2015.
The neuronal response at extended timescales: a linearized spiking input-output relation, D. Soudry and R. Meir, Front. Comput. Neurosci., 8(29), 2014