Spatially distributed and regionally unbound cellular resolution brain-wide processing loops in mice

Abstract

Until recently, brain activity could be examined either globally, through regional averaging, or locally, at cellular resolution--but not both at once. As a result, regions were characterized as functionally homogeneous units while single neurons were often characterized as heterogeneously and randomly tuned. Here, we overcome this long-standing divide by leveraging the unprecedented density of International Brain Laboratory electrophysiological recordings in mice performing a sensorimotor decision-making task. Computationally integrating these recordings and ordering neurons by functional similarity reveals directly to the eye the full temporal arc of computations unfurling over the phases of a trial and across the brain at cellular and millisecond resolution, providing a dynamical map of the architecture of brain-wide computation. We report that one of the largest shares of brain-wide variance is explained not by task-related sensory responses, integration, or decision related signals, but by neurons encoding internally generated, trial-timed broadcast sequences that carry contextual information and are biased toward hippocampal and entorhinal involvement. We find that individual neurons reliably specialize in interpretable functions, yet neurons with similar functional roles are widely dispersed across regions. Each region contains cells spanning nearly the full repertoire of response types, rendering functional tuning largely unpredictable from cytoarchitectural boundaries or spatial location alone. In particular, even primary visual cortex contains a broad diversity of response types beyond stimulus-locked responses. The dominant discernible organizational distinction is instead between cortical and subcortical processing. Subcortical neurons carry the bulk of internal computations in expert mice, including integration, decision-making, and movement initiation. These patterns support a model of broadcast rather than staged computation, in which signals are widely shared and transformed rather than serially processed. Finally, we find that despite widespread regional mixing, representations at the single neuron level are not randomly mixed. Neurons combine specific features in highly correlated groupings, contradicting the hypothesis of random mixed selectivity at the cellular level. Together, our findings suggest a revised architecture of brain computation: typically interpretable and non-random selectivity at the level of single neurons, coupled to continual brain-wide broadcast rather than staged processing, in which internally structured activity dominates neural dynamics and scaffolds cognition.