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We present a hidden Markov model that describes variation in an animal'sposition associated with varying levels of activity in action potential spiketrains of individual place cell neurons. The model incorporates acoarse-graining of position, which we find to be a more parsimoniousdescription of the system than other models. We use a sequential Monte Carloalgorithm for Bayesian inference of model parameters, including the state spacedimension, and we explain how to estimate position from spike trainobservations (decoding). We obtain greater accuracy over other methods in theconditions of high temporal resolution and small neuronal sample size. We alsopresent a novel, model-based approach to the study of replay: the expression ofspike train activity related to behaviour during times of motionlessness orsleep, thought to be integral to the consolidation of long-term memories. Wedemonstrate how we can detect the time, information content and compressionrate of replay events in simulated and real hippocampal data recorded from ratsin two different environments, and verify the correlation between the times ofdetected replay events and of sharp wave/ripples in the local field potential.

A hidden Markov model for decoding and the analysis of replay in spike trains