Practical Neural Field Theory: A Hands-on Approach Using Python

Neural Field Theory (NFT) represents a powerful mathematical framework for understanding brain dynamics, yet its practical implementation often poses challenges for researchers. In this educational course, I will lead two hands-on sessions that bridge theoretical concepts with practical applications using interactive Python notebooks in Google Colab. Building on the success of our previous educational courses at OHBM 2023 (https://griffithslab.github.io/OHBM-whole-brain-modelling-course/) and OHBM 2024 (https://griffithslab.github.io/OHBM2024-educational-course/), these sessions will provide participants with hands-on experience in implementing and exploring NFT concepts.

The first hands-on session will focus on fundamental NFT implementations, following the morning's theoretical foundations. Participants will learn to construct basic neural field models, integrate them with connectome data, and visualize the resulting dynamics. The second session will build upon this foundation to explore more advanced applications, including the modeling of brain rhythms and cortico-hippocampal interactions. Both sessions will utilize pre-prepared Jupyter notebooks, allowing participants to modify parameters and immediately observe their effects on brain dynamics.

These interactive sessions are designed to make NFT accessible to researchers from diverse backgrounds, from those new to computational modeling to experienced modelers looking to incorporate NFT into their work.
Following OHBM's commitment to open science and reproducibility, all materials will be freely available through Google Colab. Our previous workshops' materials remain openly accessible, demonstrating our ongoing commitment to the open-science initiative:

OHBM 2023 (Montreal):
https://drive.google.com/drive/folders/1Dnk5HyudcVPXVNT3l7E1FwW1FVIf01Lg

OHBM 2024 (Seoul):
https://drive.google.com/drive/folders/1uR-NMDuaQtJs0wJAwG8NKWQ9x5xbeysQ

Through these hands-on sessions, participants will gain practical experience with NFT implementations that they can directly apply to their own research questions, while contributing to a growing community of open-source computational neuroscience.