论文标题
基于活动的重新布线的神经网络中的自组织批判性
Self-organized criticality in neural networks from activity-based rewiring
论文作者
论文摘要
神经系统在沉默和混乱动力学之间的动态状态下处理信息。这导致了批判性假设，这表明神经系统通过向动力学相变的临界点进行自组织来达到这样的状态。在这里，我们研究了一个最小的神经网络模型，该模型在存在随机噪声的情况下使用仅利用局部信息的自随机噪声表现出自组织的关键性。对于网络演变，取决于节点的平均活动，将传入链接添加到节点或删除。仅基于此重新布线，网络向危险态发展，显示了典型的幂律分布式雪崩统计。观察到的指数与动态缩放理论所预测的临界性以及观察到的神经雪崩指数相符。无需参数调整即可自动达到模型的临界状态，与初始条件无关，在随机噪声下是稳健的，并且独立于实现的细节，因为该模型的不同变体表示。我们认为，这支持了以下假设：真正的神经系统可以利用相似的机制来自我组织，尤其是在早期发育阶段。
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 towards 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 towards a critcal 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 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 similar mechanisms to self-organize towards criticality especially during early developmental stages.
