Combinatorial analysis of sequential firing patterns across multiple neurons decoding memory of sequential spatial experience in rat hippocampus

Abstract

There is broad agreement that the hippocampus is crucially involved in the formation of richly-detailed, long term memories of events in humans. A key aspect of such memories is the temporal order and spatial context of the events experienced. Evidence from a wide variety of behavioral and electrophysiological experiments indicates that the rodent hippocampal spatial memory system is a model system for studying this type of memory in humans. Here, we develop a new combinatorial method for analyzing sequential firing patterns involving an arbitrary number of neurons based on relative time order. We then apply this method to decode memories of sequential spatial experience in the rat hippocampus during slow wave sleep. Specificaly, rats are trained to repeatedly run through a sequence of spatial receptive fields ("place fields") of hippocampal CA1 "place cells" in a fixed temporal order. The spiking activity of many such individual cells is recorded before (PRE), during (RUN), and after (POST) this experience. By treating each place field traversed as an individual event, the rat's experience in RUN can be represeted by the resulting sequence of place fields traversed, and therefore by the activity of the corresponding place cells. Then to characterize the extent to which the sequential nature of the RUN experience has been encoded into memory, we search for firing patterns related to the RUN sequence in POST. To do so, we develop a method that statistically quantifies the similarity between any desired "reference sequence" (here chosen to be the RUN sequence) and arbitrary temporal firing patterns. We find that the RUN sequence is repeatedly re-expressed during POST slow wave sleep in brief bursts involving four or more cells firing in order, but not so during PRE.

(cont.) This provides direct neural evidence of the rapid learning of extended spatial sequences experienced in RUN. The results may shed light on the encoding of memories of events in time ("episodic memories") in humans. Furthermore, the multiple spike train analysis method developed here is general and could be applied to many other neural systems in many different experimental conditions.