Black Dog Institute and School of Psychiatry, University of New South Wales, Sydney, Australia.
PLoS Comput Biol. 2011 Jun;7(6):e1002038. doi: 10.1371/journal.pcbi.1002038. Epub 2011 Jun 2.
Self-organized criticality refers to the spontaneous emergence of self-similar dynamics in complex systems poised between order and randomness. The presence of self-organized critical dynamics in the brain is theoretically appealing and is supported by recent neurophysiological studies. Despite this, the neurobiological determinants of these dynamics have not been previously sought. Here, we systematically examined the influence of such determinants in hierarchically modular networks of leaky integrate-and-fire neurons with spike-timing-dependent synaptic plasticity and axonal conduction delays. We characterized emergent dynamics in our networks by distributions of active neuronal ensemble modules (neuronal avalanches) and rigorously assessed these distributions for power-law scaling. We found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics. Importantly, modular connectivity and low wiring cost broadened this transition, and enabled a regime indicative of self-organized criticality. The regime only occurred when modular connectivity, low wiring cost and synaptic plasticity were simultaneously present, and the regime was most evident when between-module connection density scaled as a power-law. The regime was robust to variations in other neurobiologically relevant parameters and favored systems with low external drive and strong internal interactions. Increases in system size and connectivity facilitated internal interactions, permitting reductions in external drive and facilitating convergence of postsynaptic-response magnitude and synaptic-plasticity learning rate parameter values towards neurobiologically realistic levels. We hence infer a novel association between self-organized critical neuronal dynamics and several neurobiologically realistic features of structural connectivity. The central role of these features in our model may reflect their importance for neuronal information processing.
自组织临界性是指复杂系统在秩序和随机性之间处于平衡状态时，自发出现自相似动力学的现象。大脑中存在自组织临界动力学在理论上是有吸引力的，并得到了最近神经生理学研究的支持。尽管如此，这些动力学的神经生物学决定因素尚未被研究过。在这里，我们系统地研究了在具有尖峰时间依赖性突触可塑性和轴突传导延迟的渗漏积分和放电神经元的层次模块化网络中，这些决定因素的影响。我们通过活跃神经元集合模块（神经元瀑流）的分布来描述我们网络中的涌现动力学，并严格评估这些分布的幂律标度。我们发现，尖峰时间依赖性突触可塑性使随机亚临界动力学向有序超临界动力学的快速相变成为可能。重要的是，模块连接和低布线成本拓宽了这种转变，并使自组织临界性的状态成为可能。只有当模块连接、低布线成本和突触可塑性同时存在时，才会出现这种状态，并且当模块间连接密度按幂律缩放时，这种状态最为明显。这种状态对其他与神经生物学相关的参数的变化具有鲁棒性，并且有利于具有低外部驱动和强内部相互作用的系统。系统规模和连接性的增加促进了内部相互作用，从而允许减少外部驱动，并促进突触后响应幅度和突触可塑性学习率参数值向神经生物学现实水平收敛。因此，我们推断出自组织临界神经元动力学与结构连接的几个神经生物学现实特征之间存在新的关联。这些特征在我们的模型中起着核心作用，这可能反映了它们对神经元信息处理的重要性。
