Hippocampal formation as unitary coherent particle filter

A standard approach to localization, recall and prediction from sensors is the Hidden Markov Model (HMM, [1]), with location as hidden state and sense data as observations. Assuming that hippocampus performs a similar task, we present a new top-down mapping of this function onto its anatomy. In localization models of CA3 [2], firing rates of individual place cells encode probabilities over current location and recurrent connections may represent transitions probabilities between them. In auto-associative models [3], recurrent connections bring the network into a vector-coded memorized state. The existence of cells encoding rewards and locations of external objects supports this view [3].