ROOTS: An Algorithm to Generate Biologically Realistic Cortical Axons (Bingham et al 2020)

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"... a Ruled-Optimum Ordered Tree System (ROOTS) was developed that extends the capability of neuronal morphology generative methods to include highly branched cortical axon terminal arbors. Further, this study presents and explores a clear use-case for such models in the prediction of cortical tissue response to externally applied electric fields. The results presented herein comprise (i) a quantitative and qualitative analysis of the generative algorithm proposed, (ii) a comparison of generated fibers with those observed in histological studies, (iii) a study of the requisite spatial and morphological complexity of axonal arbors for accurate prediction of neuronal response to extracellular electrical stimulation, and (iv) an extracellular electrical stimulation strength–duration analysis to explore probable thresholds of excitation of the dentate perforant path under controlled conditions. ..."
1 . Bingham CS, Mergenthal A, Bouteiller JC, Song D, Lazzi G, Berger TW (2020) ROOTS: An Algorithm to Generate Biologically Realistic Cortical Axons and an Application to Electroceutical Modeling Frontiers in Computational Neuroscience 14:13
Model Information (Click on a link to find other models with that property)
Model Type: Axon;
Brain Region(s)/Organism:
Cell Type(s): Entorhinal cortex stellate cell;
Gap Junctions:
Simulation Environment: Python (web link to model);
Model Concept(s): Methods;
Implementer(s): Bingham, Clayton S [clayton.bingham at];
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