The hidden layer associative memory model of hippocampus

An earlier model introduced by the authors (1999) for fast associative memory has shown to be an efficient solution to the storage of binary patterns and the recall from incomplete input. We extend this model to include more biologically realistic constraints to serve as a model for the hippocampus. Among the constraints considered are the limited overall connectivity between the neurons and the distributed processing in a sequence of layered topographically connected maps. Although not all biophysical and modulatory effects from various sources have been incorporated into the present model, the emergent computational function of the hippocampus as a fast storage mechanism with reliable retrieval and pattern completion abilities from partial cues is the main subject of our study. We show that the proposed multiple layer mechanism employing a sparse code and a k-winner-take-all mechanism for the storage and retrieval of binary patterns can be matched to the functional layers of the hippocampus, thereby predicting computational roles for each map and an overall processing principle