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

TL;DR

This paper introduces a hidden Markov model for decoding animal position from spike trains, improving accuracy in high-resolution conditions and providing a new method to analyze replay events linked to memory consolidation.

Contribution

The paper presents a novel HMM-based approach for decoding and analyzing replay in spike trains, with Bayesian inference and detection of replay events in hippocampal data.

Findings

Enhanced decoding accuracy in high temporal resolution scenarios

Effective detection of replay events and their timing

Correlation between replay events and sharp wave/ripples

Abstract

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.