Accurately estimating neuronal correlation requires a new spike-sorting paradigm

Valérie Ventura, Richard Gerkin

Research output: Contribution to journalArticle

17 Citations (Scopus)

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

Fingerprint

Neurons
Neurophysiology

Keywords

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

ASJC Scopus subject areas

  • General

Cite this

Accurately estimating neuronal correlation requires a new spike-sorting paradigm. / Ventura, Valérie; Gerkin, Richard.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 109, No. 19, 08.05.2012, p. 7230-7235.

Research output: Contribution to journalArticle

@article{859b71047a5d4f488028db04488e8f18,
title = "Accurately estimating neuronal correlation requires a new spike-sorting paradigm",
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.",
keywords = "Clustering, Correlated spikes, Point process history, Statistical bias, Statistical power",
author = "Val{\'e}rie Ventura and Richard Gerkin",
year = "2012",
month = "5",
day = "8",
doi = "10.1073/pnas.1115236109",
language = "English (US)",
volume = "109",
pages = "7230--7235",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
number = "19",

}

TY - JOUR

T1 - Accurately estimating neuronal correlation requires a new spike-sorting paradigm

AU - Ventura, Valérie

AU - Gerkin, Richard

PY - 2012/5/8

Y1 - 2012/5/8

N2 - 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.

AB - 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.

KW - Clustering

KW - Correlated spikes

KW - Point process history

KW - Statistical bias

KW - Statistical power

UR - http://www.scopus.com/inward/record.url?scp=84860830731&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84860830731&partnerID=8YFLogxK

U2 - 10.1073/pnas.1115236109

DO - 10.1073/pnas.1115236109

M3 - Article

C2 - 22529350

AN - SCOPUS:84860830731

VL - 109

SP - 7230

EP - 7235

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

SN - 0027-8424

IS - 19

ER -