Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK.
Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, 33 Saripolou Street, 3036, Limassol, Cyprus.
Sci Rep. 2022 Feb 21;12(1):2896. doi: 10.1038/s41598-022-06573-1.
Methods on modelling the human brain as a Complex System have increased remarkably in the literature as researchers seek to understand the underlying foundations behind cognition, behaviour, and perception. Computational methods, especially Graph Theory-based methods, have recently contributed significantly in understanding the wiring connectivity of the brain, modelling it as a set of nodes connected by edges. Therefore, the brain's spatiotemporal dynamics can be holistically studied by considering a network, which consists of many neurons, represented by nodes. Various models have been proposed for modelling such neurons. A recently proposed method in training such networks, called full-Force, produces networks that perform tasks with fewer neurons and greater noise robustness than previous least-squares approaches (i.e. FORCE method). In this paper, the first direct applicability of a variant of the full-Force method to biologically-motivated Spiking RNNs (SRNNs) is demonstrated. The SRNN is a graph consisting of modules. Each module is modelled as a Small-World Network (SWN), which is a specific type of a biologically-plausible graph. So, the first direct applicability of a variant of the full-Force method to modular SWNs is demonstrated, evaluated through regression and information theoretic metrics. For the first time, the aforementioned method is applied to spiking neuron models and trained on various real-life Electroencephalography (EEG) signals. To the best of the authors' knowledge, all the contributions of this paper are novel. Results show that trained SRNNs match EEG signals almost perfectly, while network dynamics can mimic the target dynamics. This demonstrates that the holistic setup of the network model and the neuron model which are both more biologically plausible than previous work, can be tuned into real biological signal dynamics.
作为研究人员试图理解认知、行为和感知背后的基础，将人类大脑建模为复杂系统的方法在文献中显著增加。计算方法，特别是基于图论的方法，最近在理解大脑的布线连接方面做出了重大贡献，将大脑建模为一组由边缘连接的节点。因此，可以通过考虑由节点表示的许多神经元组成的网络来整体研究大脑的时空动力学。已经提出了各种用于建模此类神经元的模型。最近提出的一种用于训练此类网络的方法，称为全力，与之前的最小二乘法（即 FORCE 方法）相比，产生了具有更少神经元和更大噪声鲁棒性的网络。在本文中，首次证明了全力方法的变体在具有生物动机的尖峰递归神经网络（SRNN）中的直接适用性。SRNN 是由模块组成的图。每个模块都建模为小世界网络（SWN），这是一种具有生物合理性的特定类型的图。因此，首次证明了全力方法的变体在模块化 SWN 中的直接适用性，并通过回归和信息论指标进行了评估。首次将上述方法应用于尖峰神经元模型，并在各种实际脑电（EEG）信号上进行了训练。据作者所知，本文的所有贡献都是新颖的。结果表明，经过训练的 SRNN 几乎可以完美匹配 EEG 信号，同时网络动态可以模拟目标动态。这表明网络模型和神经元模型的整体设置比以前的工作更具有生物合理性，可以调整为真实的生物信号动态。
