论文标题
从测量中揭示皮质神经元网络的有效连通性
Revealing directed effective connectivity of cortical neuronal networks from measurements
论文作者
论文摘要
在对生物网络的研究中,主要挑战之一是了解网络结构与动态之间的关系。在本文中,我们在体外皮质神经元培养物中将其建模为随机动力学系统,并应用了一种从动力学[Ching and Tam,phys。重建定向网络的方法。 Rev. E 95,010301(R),2017年]揭示了有效的连通性,即来自多电极阵列记录的电压测量的神经元培养物的定向链接和突触权重。如此获得的有效连通性重现了大鼠和猴子皮质区域的几个特征,并且具有与线虫C.秀丽隐杆线虫的突触网络相似的网络特性,线虫秀丽隐杆线虫是唯一的生物体,其整个神经系统截至今天。传入程度的分布是双峰的,平均传入和外向强度的分布是无高斯的,尾巴长。有效的连通性捕获了使用峰值活动之间的统计相关性估算的常用功能连接性中的不同信息。发现兴奋性传入和传出联系的平均突触强度随着估计的有效连通性的尖峰活动而增加,而不是使用相同的电压测量值估算的功能连通性。因此,这些结果表明,重建的有效连通性可以捕获突触连接的一般特性,并更好地揭示网络结构与动态之间的关系。
In the study of biological networks, one of the major challenges is to understand the relationships between network structure and dynamics. In this paper, we model in vitro cortical neuronal cultures as stochastic dynamical systems and apply a method that reconstructs directed networks from dynamics [Ching and Tam, Phys. Rev. E 95, 010301(R), 2017] to reveal directed effective connectivity, namely the directed links and synaptic weights, of the neuronal cultures from voltage measurements recorded by a multielectrode array. The effective connectivity so obtained reproduces several features of cortical regions in rats and monkeys and has similar network properties as the synaptic network of the nematode C. elegans, the only organism whose entire nervous system has been mapped out as of today. The distribution of the incoming degree is bimodal and the distributions of the average incoming and outgoing synaptic strength are non-Gaussian with long tails. The effective connectivity captures different information from the commonly studied functional connectivity, estimated using statistical correlation between spiking activities. The average synaptic strengths of excitatory incoming and outgoing links are found to increase with the spiking activity in the estimated effective connectivity but not in the functional connectivity estimated using the same sets of voltage measurements. These results thus demonstrate that the reconstructed effective connectivity can capture the general properties of synaptic connections and better reveal relationships between network structure and dynamics.