When random variation results in functional significance

Abstract

Many functional properties vary dramatically across neurons in cerebral cortex. Two fundamental goals of systems neuroscience are to determine which neurons execute which functions and how the different functional properties of a neuron are related. Often, it is assumed that if two properties are uncorrelated, then there is no important relationship to report. Here we show that this assumption can lead to wrong conclusions; functional segregation can emerge, by chance, due to random variation when that variation is distributed according to skewed, heavy-tailed distributions. We reinterpret the results we previously reported in Kells et al 2019 [1], showing that they are a prime example of functional segregation due to random variation.