Accurately estimating neuronal correlation requires a new spike-sorting paradigm

Valérie Ventura, Richard C. Gerkin

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

Neurophysiology is increasingly focused on identifying coincident activity among neurons. Strong inferences about neural computation are made from the results of such studies, so it is important that these results be accurate. However, the preliminary step in the analysis of such data, the assignment of spike waveforms to individual neurons ("spike-sorting"), makes a critical assumption which undermines the analysis: that spikes, and hence neurons, are independent. We show that this assumption guarantees that coincident spiking estimates such as correlation coefficients are biased. We also show how to eliminate this bias. Our solution involves sorting spikes jointly, which contrasts with the current practice of sorting spikes independently of other spikes. This new "ensemble sorting" yields unbiased estimates of coincident spiking, and permits more data to be analyzed with confidence, improving the quality and quantity of neurophysiological inferences. These results should be of interest outside the context of neuronal correlations studies. Indeed, simultaneous recording of many neurons has become the rule rather than the exception in experiments, so it is essential to spike sort correctly if we are to make valid inferences about any properties of, and relationships between, neurons.

Original languageEnglish (US)
Pages (from-to)7230-7235
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume109
Issue number19
DOIs
StatePublished - May 8 2012
Externally publishedYes

Keywords

  • Clustering
  • Correlated spikes
  • Point process history
  • Statistical bias
  • Statistical power

ASJC Scopus subject areas

  • General

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