Speaker
Description
How does single-neuron selectivity shape population dimensionality and computational flexibility? I will address this question by combining theory with recordings from over 14,000 neurons across 43 cortical regions (IBL brain-wide map dataset). We find that functional organization depends on scale: across the cortex, selectivity reflects anatomical connectivity, whereas within individual regions, distinct functional classes ("categorical" representations) are rare, and responses are highly diverse. This diversity supports high-dimensional population representations, enabling simple linear readouts to separate experimental conditions in many different ways. Along the sensory-cognitive hierarchy, functional clustering decreases, and population dimensionality increases. Yet, after accounting for the information encoded by each region, separability is near maximal across almost all areas. These findings link single-neuron response diversity to population computation, revealing how regional specialization coexists with a shared capacity for flexible readout.