Repository containing the source code behind the manuscript Synaptic Scaling Improves the Stability of Neural Mass Models Capable of Simulating Brain Plasticity published in the Neural Computation journal.
Jure Demšar, Faculty of Computer and Information Science and Psychology Department at the Faculty of Arts, University of Ljubljana, Slovenia
Rob Forsyth, Institute of Neuroscience, Newcastle University, UK
Neural mass models offer a way of studying the development and behaviour of large-scale brain networks through computer simulations. Such simulations are currently mainly research tools but as they improve, they could soon play a role in understanding, predicting and optimising patient treatments, particularly in relation to effects and outcomes of brain injury. To bring us closer to this goal we took an existing state-of-the-art neural mass model capable of simulating connection growth through simulated plasticity processes. We identified and addressed some of the model's limitations by implementing biologically plausible mechanisms. The main limitation of the original model was its instability, which we addressed by incorporating a representation of the mechanism of synaptic scaling and examining the effects of optimising parameters in the model. We show that the updated model retains all the merits of the original model, while being more stable and capable of generating networks that are in several aspects similar to those found in real brains.