Understanding how sensory information is represented, processed, and leads to generation of complex behavior is the major goal of systems neuroscience. However, the ability to detect and manipulate such large-scale functional circuits has been hampered by the lack of appropriate tools and methods that allow for parallel recording of neuronal population activity at high spatial and temporal resolutions. We have recently demonstrated near-simultaneous recording from over 1 million neurons distributed across both hemispheres and different layers of the mouse cortex at cellular resolution. At the same time the widespread application of dimensionality reduction tools to such data implies that neural dynamics can be approximated by low-dimensional “latent” signals reflecting neural computations. However, what would be the biological utility of such a redundant encoding scheme, and what is the appropriate resolution and scale of recording to understand brain function? Imaging neural activity at cellular resolution and near-simultaneously across mouse cortex, we have recently found unbounded scaling of dimensionality of neuronal population activity with neuron number in populations sizes of up to one million neurons. Our data suggests that while half of the neural variance is contained within about sixteen dimensions that are correlated with behavior, the majority of the reliable dimensions which collectively account for the other half of total neuronal variance do not have any immediate behavioral or sensory correlates. The activity patterns underlying these higher dimensions are fine-grained and cortex-wide, highlighting that large-scale, cellular-resolution recording is required to uncover the full substrates of neuronal computations.