Abstract
Correlated variability in neuronal activity (spike count correlations, r(SC)) can constrain how information is read out from populations of neurons. Traditionally, r(SC) is reported as a single value summarizing a brain area. However, single values, like summary statistics, stand to obscure underlying features of the constituent elements. We predict that in brain areas containing distinct neuronal subpopulations, different subpopulations will exhibit distinct levels of r(SC) that are not captured by the population r(SC). We tested this idea in macaque superior colliculus (SC), a structure containing several functional classes (i.e., subpopulations) of neurons. We found that during saccade tasks, different functional classes exhibited differing degrees of r(SC). "Delay class" neurons displayed the highest r(SC), especially during saccades that relied on working memory. Such dependence of r(SC) on functional class and cognitive demand underscores the importance of taking functional subpopulations into account when attempting to model or infer population coding principles.