Accounting for Biases in the Estimation of Neuronal Signal Correlation.

ABSTRACT: Signal correlation (r _s) is commonly defined as the correlation between the tuning curves of two neurons and is widely used as a metric of tuning similarity. It is fundamental to how populations of neurons represent stimuli and has been central to many studies of neural coding. Yet the classic estimate, Pearson's correlation coefficient, [Formula: see text], between the average responses of two neurons to a set of stimuli suffers from confounding biases. The estimate [Formula: see text] can be downwardly biased by trial-to-trial variability and also upwardly biased by trial-to-trial correlation between neurons, and these biases can hide important aspects of neural coding. Here we provide analytic results on the source of these biases and explore them for ranges of paramet

SUBMITTER: Pospisil DA 

PROVIDER: S-EPMC8244973 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature