McDonnell Mark D, Ward Lawrence M
Computational and Theoretical Neuroscience Laboratory, Institute for Telecommunications Research, University of South Australia, Mawson Lakes, South Australia, Australia.
Department of Psychology and Brain Research Centre, University of British Columbia, Vancouver, British Columbia, Canada.
PLoS One. 2014 Apr 17;9(4):e88254. doi: 10.1371/journal.pone.0088254. eCollection 2014.
Directed random graph models frequently are used successfully in modeling the population dynamics of networks of cortical neurons connected by chemical synapses. Experimental results consistently reveal that neuronal network topology is complex, however, in the sense that it differs statistically from a random network, and differs for classes of neurons that are physiologically different. This suggests that complex network models whose subnetworks have distinct topological structure may be a useful, and more biologically realistic, alternative to random networks. Here we demonstrate that the balanced excitation and inhibition frequently observed in small cortical regions can transiently disappear in otherwise standard neuronal-scale models of fluctuation-driven dynamics, solely because the random network topology was replaced by a complex clustered one, whilst not changing the in-degree of any neurons. In this network, a small subset of cells whose inhibition comes only from outside their local cluster are the cause of bistable population dynamics, where different clusters of these cells irregularly switch back and forth from a sparsely firing state to a highly active state. Transitions to the highly active state occur when a cluster of these cells spikes sufficiently often to cause strong unbalanced positive feedback to each other. Transitions back to the sparsely firing state rely on occasional large fluctuations in the amount of non-local inhibition received. Neurons in the model are homogeneous in their intrinsic dynamics and in-degrees, but differ in the abundance of various directed feedback motifs in which they participate. Our findings suggest that (i) models and simulations should take into account complex structure that varies for neuron and synapse classes; (ii) differences in the dynamics of neurons with similar intrinsic properties may be caused by their membership in distinctive local networks; (iii) it is important to identify neurons that share physiological properties and location, but differ in their connectivity.
定向随机图模型经常成功地用于对由化学突触连接的皮质神经元网络的种群动态进行建模。然而，实验结果一致表明，神经元网络拓扑结构很复杂，从统计学角度看它不同于随机网络，并且对于生理上不同的神经元类别也有所不同。这表明，其子网络具有独特拓扑结构的复杂网络模型可能是一种有用的、更符合生物学现实的随机网络替代方案。在这里，我们证明，在波动驱动动力学的其他方面标准的神经元尺度模型中，小皮质区域中经常观察到的平衡兴奋和抑制可能会暂时消失，仅仅是因为随机网络拓扑被复杂的聚类拓扑所取代，而没有改变任何神经元的入度。在这个网络中，一小部分抑制仅来自其局部聚类之外的细胞是双稳种群动态的原因，这些细胞的不同聚类会不规则地从稀疏放电状态切换到高度活跃状态。当这些细胞的一个聚类足够频繁地放电以导致彼此之间强烈的不平衡正反馈时，就会发生向高度活跃状态的转变。回到稀疏放电状态则依赖于偶尔接收到的非局部抑制量的大幅波动。模型中的神经元在其内在动力学和入度方面是同质的，但在它们参与的各种定向反馈基序的丰度上有所不同。我们的研究结果表明：（i）模型和模拟应考虑因神经元和突触类别而异的复杂结构；（ii）具有相似内在特性的神经元动力学差异可能是由于它们属于不同的局部网络；（iii）识别具有相同生理特性和位置但连接性不同的神经元很重要。
