Structured Sparsification of Signal-Transmission Networks Enhances Visual Information Coding

Abstract

The brain can rapidly adjust sensory processing according to behavioral context from moment to moment without altering its underlying anatomical wiring. Such flexibility is thought to arise from dynamic reconfiguration of the effective network through which signals propagate, yet the principles governing such reconfiguration and its consequences for coding remain unclear. Here, we exploited the distinct stationary and locomotion states within the same recording sessions and addressed this question by inferring directed, millisecond-scale signal-transmission networks at single-neuron resolution in behaving mice. Locomotion was accompanied by a counterintuitive structured sparsification of the inferred network: interactions became fewer but more temporally precise, while the remaining interactions were more local, modular, feature-specific, and feedforward. We then used theoretical analysis and controlled perturbations of multi-area rate models to systematically determine how each empirically observed component of this reorganization affects population coding. We found that sparsification reduced shared variability; local organization reduced signal-noise alignment; feature-specific interactions sharpened selectivity; and a more feedforward architecture accelerated decoding. These results provide an experimentally grounded mechanism by which behavioral state reorganizes neuronal interactions given the same sensory inputs, and suggest structured sparsification as an underlying principle of network reconfiguration that supports accurate and faster sensory coding.