Unsupervised Learning of Place Cells, Head-Direction Cells, and Spatial-View Cells with Slow Feature Analysis on Quasi-Natural Videos

Abstract

We present a model for the self-organized formation of place cells, head-direction cells, and spatial view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli. The model comprises a hierarchy of Slow Feature Analysis (SFA) nodes, which were recently shown to be a good model for complex cells in the early visual system (Berkes and Wiskott, 2005). The system extracts a distributed grid-like representation of position and orientation, which is transcoded into a localized place field, head direction, or view representation, respectively, by sparse coding. The type of cells that develops depends solely on the relevant input statistics, i.e the movement pattern of the simulated animal. The numerical simulations are complemented by a mathematical analysis that allows us to accurately predict the output of the top SFA layer.

Available Versions of this Item

• Unsupervised Learning of Place Cells, Head-Direction Cells, and Spatial-View Cells with Slow Feature Analysis on Quasi-Natural Videos. (deposited 26 Apr 2007) [Currently Displayed]
  • Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells. (deposited 12 Sep 2007)