Autoregressive model of the hippocampal representation of events

The hippocampal formation is believed to play a central role in forming long lasting representation of events. However, in contrast to the continuous nature of sensory signal flow, events are spatially and temporally bounded processes. In this paper we are interested in the kind of representation that allows for detecting and/or predicting events. Based on new results on the identification problem of linear hidden processes, we propose a general signal encoding model that can represent causal relationships used to define events. We translate the model into a connectionist structure in which parameter learning follows biologically plausible rules. We also speculate on the resemblance of the resulting structure to the connection system of the hippocampal formation. When our signal encoding model is applied on spatially anchored inputs, its different parts feature spatially localized and periodic neural activity similar to those found in the hippocampus and in the entorhinal cortex, respectively. These emergent forms of spatial activity differentiates our model from other computational models of (spatial) memory as the model has not been explicitly designed to deal with spatial information. We speculate that our model may describe the core function of the hippocampal region in forming episodic memory and supporting spatial navigation.