A Thermodynamic Theory of Axon Guidance: Navigation Through High-Entropy Signaling States

Precise neural circuit formation requires growth cones to integrate multiple, sometimes competing, guidance signals into persistent yet adaptable movement. Here, we propose a theoretical framework that recasts axon guidance as a thermodynamically regulated computation. An artificial neural network, trained on in vivo genetic data and associated outgrowth patterns, maps the microstates of a localized guidance signaling network to macroscopic outgrowth behaviors. Using the trained model, we simulated axon pathfinding through extracellular gradients of molecular cues and created dynamic entropic landscapes. These results reveal that growth cones navigate high-entropy ridges that preserve plasticity. Navigation along these ridges supports persistent extension, whereas turning and branching occur near boundaries between competing entropic macrostates, consistent with transitions in the cytoskeletal machinery that controls growth-cone movement. More broadly, the framework proposes that robust biological patterning can emerge from microscopic variability when signaling networks operate near boundaries between competing behavioral states.