Pirino Virginia, Riccomagno Eva, Martinoia Sergio, Massobrio Paolo
Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS),University of Genova, Genova, Italy.
Phys Biol. 2015 Jan 5;12(1):016007. doi: 10.1088/1478-3975/12/1/016007.
To address the issue of extracting useful information from large data-set of large scale networks of neurons, we propose an algorithm that involves both algebraic-statistical and topological tools. We investigate the electrical behavior of in vitro cortical assemblies both during spontaneous and stimulus-evoked activity coupled to Micro-Electrode Arrays (MEAs). Our goal is to identify core sub-networks of repetitive and synchronous patterns of activity and to characterize them. The analysis is performed at different resolution levels using a clustering algorithm that reduces the network dimensionality. To better visualize the results, we provide a graphical representation of the detected sub-networks and characterize them with a topological invariant, i.e. the sequence of Betti numbers computed on the associated simplicial complexes. The results show that the extracted sub-populations of neurons have a more heterogeneous firing rate with respect to the entire network. Furthermore, the comparison of spontaneous and stimulus-evoked behavior reveals similarities in the identified clusters of neurons, indicating that in both conditions similar activation patterns drive the global network activity.
为了解决从大规模神经元网络的大数据集中提取有用信息的问题，我们提出了一种算法，该算法涉及代数统计和拓扑工具。我们研究了与微电极阵列（MEA）耦合的体外皮质组件在自发活动和刺激诱发活动期间的电行为。我们的目标是识别重复和同步活动模式的核心子网并对其进行表征。使用降低网络维度的聚类算法在不同分辨率级别上进行分析。为了更好地可视化结果，我们提供了检测到的子网的图形表示，并用拓扑不变量对其进行表征，即根据相关单纯复形计算的贝蒂数序列。结果表明，相对于整个网络，提取的神经元亚群具有更不均匀的放电率。此外，自发行为和刺激诱发行为的比较揭示了所识别的神经元簇中的相似性，表明在这两种情况下，相似的激活模式驱动全局网络活动。
