Rarely Categorical, Always High-dimensional: How the Neural Code Changes Along the Cortical Hierarchy

A long-standing debate in neuroscience concerns whether individual neurons are organized into functionally distinct populations that encode information differently ("categorical" representations [1-3]) and the implications for neural computation. Here, we systematically analyzed how cortical neurons encode cognitive, sensory, and movement variables across 43 cortical regions during a complex task (14,000+ units from the International Brain Laboratory public Brain-wide Map data set [4]) and studied how these properties change across the sensory-cognitive cortical hierarchy [5]. We found that the structure of the neural code was scale-dependent: on a whole-cortex scale, neural selectivity was categorical and organized across regions in a way that reflected their anatomical connectivity. However, within individual regions, categorical representations were rare and limited to primary sensory areas. Remarkably, the degree of categorical clustering of neural selectivity was inversely correlated to the dimensionality of neural representations, suggesting a link between single-neuron selectivity and computational properties of population codes that we explained in a mathematical model. Finally, we found that the fraction of linearly separable combinations of experimental conditions ("Shattering Dimensionality" [6]) was near maximal across all areas, indicating a robust and uniform ability for flexible information encoding throughout the cortex. In conclusion, our results provide systematic evidence for a non-categorical, high-dimensional neural code in all but the lower levels of the cortical hierarchy.