ROOTS: An Algorithm to Generate Biologically Realistic Cortical Axons and an Application to Electroceutical Modeling.

摘要: Advances in computation and neuronal modeling have enabled the study of entire neural tissue systems with an impressive degree biological realism. These efforts focused largely on dendrites somas while neglecting axons. The need for biologically realistic explicit axonal models is particularly clear applications involving clinical therapeutic electrical stimulation because axons are generally more excitable than other neuroanatomical subunits. While many can rely existing repositories reconstructed dendritic/somatic morphologies to real cells or estimate parameters a generative model, such datasets scarce incomplete. Those that do exist may still be insufficient build accurate increased geometric variability demands proportional increase data. To address this need, Ruled-Optimum Ordered Tree System (ROOTS) was developed extends capability morphology methods include highly branched cortical axon terminal arbors. Further, presents explores use-case prediction response externally applied electric fields. results presented herein comprise (i) quantitative qualitative analysis algorithm proposed, (ii) comparison generated fibers those observed histological studies, (iii) requisite spatial morphological complexity arbors extracellular stimulation, (iv) strength-duration explore probable thresholds excitation dentate perforant path under controlled conditions. ROOTS demonstrates superior ability capture realism model fibers, allowing improved accuracy predicting impact microscale structures branching patterns spatiotemporal activity presence