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

Marc Box, Matt Jones, Nick Whiteley*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)
360 Downloads (Pure)


We present a hidden Markov model that describes variation in an animal’s position associated with varying levels of activity in action potential spike trains of individual place cell neurons. The model incorporates a coarse-graining of position, which we find to be a more parsimonious description of the system than other models. We use a sequential Monte Carlo algorithm for Bayesian inference of model parameters, including the state space dimension, and we explain how to estimate position from spike train observations (decoding). We obtain greater accuracy over other methods in the conditions of high temporal resolution and small neuronal sample size. We also present a novel, model-based approach to the study of replay: the expression of spike train activity related to behaviour during times of motionlessness or sleep, thought to be integral to the consolidation of long-term memories. We demonstrate how we
can detect the time, information content and compression rate of replay events in simulated and real hippocampal data recorded from rats in two different environments, and verify the correlation between the times of detected replay events and of sharp wave/ripples in the local field potential.
Original languageEnglish
Pages (from-to)339-366
Number of pages28
JournalJournal of Computational Neuroscience
Issue number3
Early online date13 Sept 2016
Publication statusPublished - 1 Dec 2016


  • Decoding
  • Hidden markov model
  • Hippocampus
  • Spike train modelling
  • Spike train replay


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