Stimulus-Driven Thermodynamic Shifts and Geometric Reorganization in Mouse Primary Visual Cortex
Efficient coding is essential for sensory systems to extract meaningful information from the environment. Here, we investigate how stimulus-driven thermodynamic shifts and geometric reorganization enable efficient population coding. Using wide-field calcium imaging, we simultaneously recorded neuronal activity across the entire mouse V1 under the presentation of structured stimuli and characterized neural dynamics spanning microscale neuronal connectivity, mesoscale thermodynamic states, and macroscale manifold geometry. We found that stimulus presentation reorganized neuronal couplings into increasingly modular subnetworks, driving a shift from near-critical dynamics toward a more ordered regime. This shift coincided with a compression of neural population activity onto low-dimensional manifolds aligned with stimulus features, thereby enabling efficient coding. Furthermore, mathematical derivations and in silico perturbation experiments confirmed that selective modulations of neuronal connectivity altered both critical temperature and manifold geometry, establishing a causal link from neuronal couplings to efficient coding. Collectively, our findings suggest a mechanistic bridge between statistical physics and neural geometry, providing new theoretical insights into how neural networks dynamically transition into more stable and efficient coding states in response to the environment.