Detection and Removal of Hyper-synchronous Artifacts in Massively Parallel Spike Recordings

Abstract

SummaryCurrent electrophysiology experiments often involve massively parallel recordings of neuronal activity using multi-electrode arrays. While researchers have been aware of artifacts arising from electric cross-talk between channels in setups for such recordings, systematic and quantitative assessment of the effects of those artifacts on the data quality has never been reported. Here we present, based on examination of electrophysiology recordings from multiple laboratories, that multielectrode recordings of spiking activity commonly contain extremely precise (at the data sampling resolution) spike coincidences far above the chance level. We derive, through modeling of the electric cross-talk, a systematic relation between the amount of such hyper-synchronous events (HSEs) in channel pairs and the correlation between the raw signals of those channels in the multi-unit activity frequency range (250-7500 Hz). Based on that relation, we propose a method to identify and exclude specific channels to remove artifactual HSEs from the data. We further demonstrate that the artifactual HSEs can severely affect various types of analyses on spike train data. Taken together, our results warn researchers to pay considerable attention to the presence of HSEs in spike train data and to make efforts to remove the artifactual ones from the data to avoid false results.