Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia, and MBLab, Department of Psychology, Faculty of Arts, University of Ljubljana, Slovenia
Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE1 4LP, U.K.
Neural Comput. 2020 Feb;32(2):424-446. doi: 10.1162/neco_a_01257. Epub 2019 Dec 13.
Neural mass models offer a way of studying the development and behavior 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 optimizing 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 optimizing 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.
神经群体模型为通过计算机模拟研究大规模脑网络的发展和行为提供了一种方法。这种模拟目前主要是研究工具，但随着它们的改进，它们可能很快在理解、预测和优化患者治疗方面发挥作用，特别是在与脑损伤的影响和结果有关的方面。为了使我们更接近这一目标，我们采用了现有的最先进的神经群体模型，该模型能够通过模拟可塑性过程来模拟连接的增长。我们通过实现生物上合理的机制来识别和解决模型的一些限制。原始模型的主要限制是其不稳定性，我们通过引入突触缩放机制的表示并检查模型中参数优化的效果来解决这个问题。我们表明，更新后的模型保留了原始模型的所有优点，同时更加稳定，并且能够生成在几个方面与真实大脑相似的网络。
