Uncovering representations of sleep-associated hippocampal ensemble spike activity.

Andres D Grosmark,Hector Penagos,Zhe Chen,Matthew A Wilson

doi:10.1038/srep32193

Abstract

Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness.

Highlights

Recording environment open field open field circular track circular track rest/sleep box follows a lognormal distribution: strongly synchronized events are interspersed irregularly among many medium and small-sized events[25]
One decoding method is based on topographic or receptive field representations[21,22], while the other is based on topological representation without a priori measure of place receptive fields[28,29,30]
To analyze rat hippocampal ensemble spike data, we considered two model-based Bayesian decoding methods based on different statistical assumptions (Methods, Supplementary Fig. 2)

Summary

Introduction

Recording environment open field open field circular track circular track rest/sleep box follows a lognormal distribution: strongly synchronized events are interspersed irregularly among many medium and small-sized events[25]. We investigate these important statistical issues in greater detail by applying two neural population decoding methods to rat hippocampal ensemble spike data recorded in different states. We first create “synthetic” sleep data by binning and resampling spike trains obtained during active locomotion to simulate important factors that characterize SPW-ripple events, and compare the resulting decoded spatial representations to the animal’s actual run trajectory. This allows us to test two important questions of hippocampal population codes related to sleep and memory replay: representation power (“how reliably is the spatial environment represented?”) and detection power (“how can one detect significant spatial or behavioral state sequences?”). We use rat hippocampal ensemble recordings in two- and one-dimensional spaces to investigate these questions separately, and we further compare the performance of topographic vs. topological representation-based decoding methods to SPW-ripple associated spike data

Methods

Results

Conclusion