标题： 神经网络模型中的自组织临界性 显示英文标题

标题： Self-organized criticality in neural network models

摘要： 长期以来，人们一直认为神经网络必须建立并维持一定的中间活动水平，以避免陷入混沌和静默状态。在大鼠皮层培养物中，Beggs和Plenz（2003）首次观察到了临界性的强烈证据，随后在许多其他实验设置中也观察到了类似现象。这些发现引发了对神经网络中临界性和级联动力学的理论模型的深入研究，通常通过调整突触耦合将某种动态序参数反馈到网络拓扑结构中。我们在这里概述了现有的动态网络理论模型。虽然大多数模型强调生物学和神经生理学细节，但我们的方法有所不同：我们重新审视Bornholdt和Roehl（2001）早期的自组织临界神经网络模型，并在实验数据的背景下测试其适用性。保持早期模型的简单性，同时克服了相对于生物系统而言的自旋表述的缺点，我们研究了一个改进的模型（Rybarsch和Bornholdt，2012b），并表明该模型能够适应临界性，表现出与实验数据相当的级联统计特性，而无需进行参数调节。 显示英文摘要

摘要： It has long been argued that neural networks have to establish and maintain a certain intermediate level of activity in order to keep away from the regimes of chaos and silence. Strong evidence for criticality has been observed in terms of spatio-temporal activity avalanches first in cultures of rat cortex by Beggs and Plenz (2003) and subsequently in many more experimental setups. These findings sparked intense research on theoretical models for criticality and avalanche dynamics in neural networks, where usually some dynamical order parameter is fed back onto the network topology by adapting the synaptic couplings. We here give an overview of existing theoretical models of dynamical networks. While most models emphasize biological and neurophysiological detail, our path here is different: we pick up the thread of an early self-organized critical neural network model by Bornholdt and Roehl (2001) and test its applicability in the light of experimental data. Keeping the simplicity of early models, and at the same time lifting the drawback of a spin formulation with respect to the biological system, we here study an improved model (Rybarsch and Bornholdt, 2012b) and show that it adapts to criticality exhibiting avalanche statistics that compare well with experimental data without the need for parameter tuning.