BRIEF RESEARCH REPORT article
Front. Appl. Math. Stat.
Sec. Mathematical Biology
Volume 11 - 2025 | doi: 10.3389/fams.2025.1632271
Mathematical model for data-driven synthesis of neuron morphologies based on random walks
Provisionally accepted
- 1Department of Computer Science, University of Arkansas at Little Rock, Little Rock, United States
- 2Emerging Analytics Center, University of Arkansas at Little Rock, Little Rock, United States
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Recent advances in computational resources have enabled the development of large-scale, biophysically detailed brain models, which require numerous three-dimensional neuron morphologies exhibiting realistic cell-to-cell variability. However, the limited availability of experimental reconstructions restricts parameter estimation for many morphology synthesis algorithms, which typically rely on extensive datasets. Here, we propose enhancing our branching-and-annihilating random walk method by incorporating a set of mathematical equations that estimate branching and annihilation probabilities directly from Sholl plots and branch point counts. Because these morphological metrics are commonly reported in the literature, our approach facilitates the generation of realistic three-dimensional morphologies even in the absence of experimental reconstructions.
Keywords: neuron morphologies, Random walks, Morphological synthesis, neuron simulation, Galton - Watson process
Received: 20 May 2025; Accepted: 03 Oct 2025.
Copyright: © 2025 Cavarretta. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Francesco Cavarretta, fcavarretta@ualr.edu
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.