Information read-out from populations of neurons depends strongly on the correlated variability within a population, termed r_SC (spike count correlations). Traditionally, r_SC is reported as a single value for a population of neurons comprising a brain area. However, single values, like summary statistics, can obscure underlying features of the constituent elements. We predict that in brain areas consisting of distinct subpopulations of neurons, different subpopulations will exhibit distinct levels of r_SC not captured by the population r_SC. We tested this idea by recording from neurons in macaque superior colliculus (SC), a structure that contains several subpopulations (or classes) of neurons based on function. We found that during guided saccade tasks, different classes of neurons exhibited differing degrees of r_SC, which could not be attributed to variations in behavior. Neurons belonging to the “Delay” class displayed a particularly high r_SC, especially during the delay epoch of tasks that relied on visual working memory. Our finding that r_SC depends on functional class, as well as the type of task, demonstrate the importance of taking functional subpopulations into account when attempting to model or infer population coding principles in primate SC, or other areas of the brain that contain subpopulations supporting specific functions.

See methods section of paper:

https://www.biorxiv.org/content/10.1101/2021.09.15.460545v1.abstract