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

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• 作者：Marc Box ; Matt W. Jones ; Nick Whiteley
• 关键词：Spike train modelling ; Hidden markov model ; Hippocampus ; Decoding ; Spike train replay
• 刊名：Journal of Computational Neuroscience
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：41
• 期：3
• 页码：339-366
• 全文大小：2,112 KB
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biomedicine
  Neurosciences
  Neurology
  Human Genetics
  Theory of Computation
  
• 出版者：Springer Netherlands
• ISSN：1573-6873
• 卷排序：41

文摘

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.