The Axon as a Self-Modifying Computational System: Autonomous Inference, Adaptive Propagation, and AI-Enabled Mechanistic Insight

Research has demonstrated that axonal signaling processes are influenced by both static structural factors and dynamic metabolic and electro-dynamic processes. Imaging, computational modeling and research in molecular neuroscience have demonstrated that multiple processes contribute to axonal signal processing, including periodic rearrangement of cytoskeletal structures and membrane structures, and redistribution of ion channel clusters and organelles (such as mitochondria), which occur rapidly and transiently to modify excitability. The dynamics of energy production and distribution also vary between regions of the axon and at different time points during signal generation and transmission. Additionally, myelin-associated glia may temporarily modulate their metabolic and structural contributions to axonal conduction. Advanced AI-based techniques for mapping and simulating ultrastructure and the use of closed-loop perturbation experiments demonstrate that axons can generate multiple distinct electromechanical states, and therefore potentially influence both the timing of signals generated by the axon, the routing of signals to branches of the axon, and the robustness of signal propagation. While the existence of these adaptive microstates appears well established, there are many aspects of their influence on circuit level function that are poorly understood. In summary, these data support the concept that axonal conduction represents a continuum of reversible and state-dependent configurations generated by integrated interactions among molecular, structural and energetic processes. Therefore, this review will attempt to synthesize the available literature into a unified conceptual framework and identify areas of uncertainty that may direct future research into the adaptive processes underlying axonal computation.