Single-neuron network topology governs neural computation and learning in primate cortex.

Abstract

Neural networks underlie complex brain information processing, yet the role of their single-neuron topology in governing computation and behavior remains unclear, particularly regarding how it shapes individual neuron function and activity evolution during learning. Using two-photon calcium imaging, we tracked functional connectivity in thousands of posterior parietal cortex neurons as monkeys learned sensorimotor associations across days. We identified small-world networks with densely connected hub neurons that dominated encoding key task variables, driving local dynamics and neural encoding evolution during the monkeys' task performance and learning. Dynamic transitions in hub/non-hub status captured how inter-neuronal interactions shaped neuronal encoding evolution during association formation. Modular structures supported specialized neuron ensembles, enabling segregated representations and interactions within local networks. Importantly, small-world network properties predicted behavioral performance, with global information processing efficiency increasing as learning progressed. These findings reveal how single-neuron-resolution brain networks, through small-world organization, orchestrate both global and modular neural computations within local network to mediate behavior and shape learning.