Multiplex Networks of Cortical and Hippocampal Neurons Revealed at Different Timescales

Recent studies have emphasized the importance of multiplex networks – interdependent networks with shared nodes and different types of connections – in systems primarily outside of neuroscience. Though the multiplex properties of networks are frequently not considered, most networks are actually multiplex networks and the multiplex specific features of networks can greatly affect network behavior (e.g. fault tolerance). Thus, the study of networks of neurons could potentially be greatly enhanced using a multiplex perspective. Given the wide range of temporally dependent rhythms and phenomena present in neural systems, we chose to examine multiplex networks of individual neurons with time scale dependent connections. To study these networks, we used transfer entropy – an information theoretic quantity that can be used to measure linear and nonlinear interactions – to systematically measure the connectivity between individual neurons at different time scales in cortical and hippocampal slice cultures. We recorded the spiking activity of almost 12,000 neurons across 60 tissue samples using a 512-electrode array with 60 micrometer inter-electrode spacing and 50 microsecond temporal resolution. To the best of our knowledge, this preparation and recording method represents a superior combination of number of recorded neurons and temporal and spatial recording resolutions to any currently available in vivo system. We found that highly connected neurons (“hubs”) were localized to certain time scales, which, we hypothesize, increases the fault tolerance of the network. Conversely, a large proportion of non-hub neurons were not localized to certain time scales. In addition, we found that long and short time scale connectivity was uncorrelated. Finally, we found that long time scale networks were significantly less modular and more disassortative than short time scale networks in both tissue types. As far as we are aware, this analysis represents the first systematic study of temporally dependent multiplex networks among individual neurons.

近年来的研究已证实，多层网络（multiplex networks）——即具备共享节点与多种连接类型的相互依赖网络——在神经科学以外的众多系统中具有关键意义。尽管网络的多层属性常被忽略，但绝大多数网络本质上均为多层网络，且网络特有的多层特征可显著影响网络行为，例如容错性（fault tolerance）。因此，采用多层视角开展神经元网络研究，有望大幅推动该领域的研究进展。鉴于神经系统中存在大量依赖时间尺度的节律与现象，我们选择对具备时间尺度依赖型连接的单个神经元多层网络展开研究。为研究此类网络，我们采用传递熵（transfer entropy）——一种可用于度量线性与非线性相互作用的信息论量化指标——系统测定了皮层与海马脑片培养物中，不同时间尺度下单神经元之间的连接关系。我们使用电极间距为60微米、时间分辨率达50微秒的512通道电极阵列，对60份组织样本中的近12000个神经元的锋电位活动进行了记录。据我们所知，相较于当前已有的所有在体（in vivo）系统，该样本制备与记录方法在记录神经元数量、时间分辨率与空间分辨率三个维度上均实现了更优的组合。我们发现，高度连接的神经元（“中枢节点（hubs）”）会定位于特定的时间尺度，据此我们推测，这一现象可提升网络的容错性。与之相反，绝大多数非中枢节点神经元并未定位于特定的时间尺度。此外，我们还发现，长时程与短时程的连接性并无相关性。最后，我们观察到，在两种组织样本中，长时程网络的模块化程度均显著低于短时程网络，且异配性更强。据我们所知，本研究首次针对单个神经元的时间尺度依赖型多层网络开展了系统性分析。