SRNN-Brain-Modelling-Toolbox

Publication: https://www.nature.com/articles/s41598-022-06573-1

The copyright of this work submitted as part of the Master Thesis at Imperial College London, rests with the author, Georgios Ioannides. When reusing or sharing this work, ensure you credit the author clearly to others.

Acknowledgment:

Some code inspired by: Nicola & Clopath 2017, (C) Murray Shanahan et al, 2015, Eugene M. Izhikevich

Datasets taken from: S. Makeig, M. Westerfield, T.-P. Jung, J. Covington, J. Townsend, T. J. Sejnowski, and E.Courchesne, “Functionally independent components of the late positive event-related potential during visual spatial attention”,Journal of Neuroscience, vol. 19, no. 7, pp. 2665–2680,1999

Clone the repository and change the MATLAB directory to the folder in which the m-files are saved.

Running the code

For training biologically-plausible networks using the modified-Full-Force, run "main.m" within the Section3.6 folder.

For use of excitatory neurons only during training, run "FULLFORCE.m" within Section3.5 folder.

For graph theoretic experiments, run the Jupyter Notebook within Section3.1 folder.