Dual coding in an auto-associative network model of the hippocampus

Introduction The activity of pyramidal cells in the hippocampus has been empirically demonstrated to encode both spatial and non-spatial cues by means of a dual code [1]. The phase of place cell firing with respect to the theta oscillation encodes spatial information: primarily the position of an animal and its current heading [2]. Conversely, firing rate has been demonstrated to encode a variety of non-spatial cues, including running speed, complex visual stimuli and concepts [3-5]. Here we present a novel spiking neural network model which is, to our knowledge, the first to use a dual coding system in order to learn and recall associations between both temporally coded (spatial) and rate-coded (non-spatial) activity patterns.