Landmann Stefan, Baumgarten Lorenz, Bornholdt Stefan
Institut für Theoretische Physik, Universität Bremen, Germany.
Phys Rev E. 2021 Mar;103(3-1):032304. doi: 10.1103/PhysRevE.103.032304.
Neural systems process information in a dynamical regime between silence and chaotic dynamics. This has lead to the criticality hypothesis, which suggests that neural systems reach such a state by self-organizing toward the critical point of a dynamical phase transition. Here, we study a minimal neural network model that exhibits self-organized criticality in the presence of stochastic noise using a rewiring rule which only utilizes local information. For network evolution, incoming links are added to a node or deleted, depending on the node's average activity. Based on this rewiring-rule only, the network evolves toward a critical state, showing typical power-law-distributed avalanche statistics. The observed exponents are in accord with criticality as predicted by dynamical scaling theory, as well as with the observed exponents of neural avalanches. The critical state of the model is reached autonomously without the need for parameter tuning, is independent of initial conditions, is robust under stochastic noise, and independent of details of the implementation as different variants of the model indicate. We argue that this supports the hypothesis that real neural systems may utilize such a mechanism to self-organize toward criticality, especially during early developmental stages.
神经系统在寂静和混沌动力学之间的动态状态下处理信息。这导致了临界性假说，该假说认为神经系统通过向动态相变的临界点进行自组织而达到这样一种状态。在这里，我们研究一个最小神经网络模型，该模型在存在随机噪声的情况下，使用仅利用局部信息的重连规则展现出自组织临界性。对于网络演化，根据节点的平均活动，向节点添加或删除传入链接。仅基于此重连规则，网络就会朝着临界状态演化，呈现出典型的幂律分布雪崩统计特性。观测到的指数与动态标度理论预测的临界性相符，也与神经雪崩观测到的指数相符。该模型的临界状态无需参数调整即可自主达到，与初始条件无关，在随机噪声下具有鲁棒性，并且如模型的不同变体所示，与实现细节无关。我们认为这支持了这样一种假说，即真实的神经系统可能利用这种机制进行自组织以达到临界性，特别是在早期发育阶段。
