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Updated: Aug 29, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Computational methods and diffusion theory in triangulation sensing to model neuronal navigation.

Ulrich Dobramysl^1,2, David Holcman³

¹Peter Medawar Building for Pathogen Research, University of Oxford, Oxford, United Kingdom.

Reports on Progress in Physics. Physical Society (Great Britain)

|September 8, 2022

Summary

This summary is machine-generated.

Computational methods using diffusion modeling aid cell sensing and guidance by reconstructing point source locations. This approach accelerates simulations and improves accuracy in biological applications like neuronal navigation.

Keywords:

asymptotic computations cell navigation diffusion fast Brownian simulations hybrid algorithms inverse problem modeling cell sensing

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Last Updated: Aug 29, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

Area of Science:

Computational Biology
Mathematical Modeling

Background:

Computational methods are crucial for understanding biological processes across scales.
Bridging the gap between molecular and cellular levels requires advanced simulation techniques.

Purpose of the Study:

To develop and present computational approaches for cell sensing and guidance using diffusion modeling.
To reconstruct point source locations in 2D and 3D from diffusion fluxes and analyze reconstruction uncertainty.

Main Methods:

Utilized diffusion modeling, asymptotic analysis of partial differential equations, and hybrid simulation methods.
Employed explicit solutions of the Neumann-Green's function for various geometries.
Developed hybrid simulations generating Brownian paths in relevant regions and implicitly treating trajectories elsewhere.

Main Results:

Successfully reconstructed point source locations in diverse 2D and 3D scenarios.
Quantified the uncertainty in source reconstruction affected by noise.
Evaluated the impact of window configurations on source localization accuracy.

Conclusions:

The developed computational framework enhances the efficiency and accuracy of simulating cell guidance mechanisms.
The findings provide computational principles applicable to developmental biology and neuronal navigation.
Hybrid simulation strategies significantly reduce computational cost for complex biological systems.