Exploring Neural Dynamics with Time Delays: A Numerical and Bifurcation Analysis Approach
Abstract
The Wilson-Cowan model is a classic example of a ``neural mass model'' - a simplified yet powerful way to describe the activity of large networks of neurons. It is particularly interesting because, even in its relatively simple form, it can produce a surprising variety of behaviours, from steady rhythms to complex oscillations. A key factor influencing these dynamics is the presence of time delays, reflecting the time it takes for signals to travel between neurons. This article offers an accessible introduction to the Wilson-Cowan model, focusing on how these time delays affect its behaviour. We explore the core concepts behind the model, survey the existing literature, and introduce some numerical techniques specifically designed for analysing such delay differential equations. We also provide a practical, hands-on tutorial on using the DDE-BIFTOOL software package, demonstrating its application to the Wilson-Cowan model. Downloadable code allows the reader to reproduce our results and to explore the model's dynamics further, including MATLAB code for all computations, using DDE-BIFTOOL for the continuation, and Python versions of simulation codes.
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BibTeX
@misc{nelson2026exploring,
title = {{Exploring Neural Dynamics with Time Delays: A Numerical and Bifurcation Analysis Approach}},
author = {Martin R Nelson and Jonathan J Crofts},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.36920},
eprint = {2610.36920},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.36920},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Exploring Neural Dynamics with Time Delays: A Numerical and Bifurcation Analysis Approach
AU - Martin R Nelson
AU - Jonathan J Crofts
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.36920
UR - https://arxiv.org/abs/2610.36920
AB - The Wilson-Cowan model is a classic example of a ``neural mass model'' - a simplified yet powerful way to describe the activity of large networks of neurons. It is particularly interesting because, even in its relatively simple form, it can produce a surprising variety of behaviours, from steady rhythms to complex oscillations. A key factor influencing these dynamics is the presence of time delays, reflecting the time it takes for signals to travel between neurons. This article offers an accessible introduction to the Wilson-Cowan model, focusing on how these time delays affect its behaviour. We explore the core concepts behind the model, survey the existing literature, and introduce some numerical techniques specifically designed for analysing such delay differential equations. We also provide a practical, hands-on tutorial on using the DDE-BIFTOOL software package, demonstrating its application to the Wilson-Cowan model. Downloadable code allows the reader to reproduce our results and to explore the model's dynamics further, including MATLAB code for all computations, using DDE-BIFTOOL for the continuation, and Python versions of simulation codes.
ER -