GOTHAM Lab, Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain.
Departament de Física de la Matèria Condensada, Universitat de Barcelona, E-08028 Barcelona, Spain.
Chaos. 2019 Aug;29(8):083126. doi: 10.1063/1.5099038.
We study the structural and dynamical consequences of damage in spatial neuronal networks. Inspired by real in vitro networks, we construct directed networks embedded in a two-dimensional space and follow biological rules for designing the wiring of the system. As a result, synthetic cultures display strong metric correlations similar to those observed in real experiments. In its turn, neuronal dynamics is incorporated through the Izhikevich model adopting the parameters derived from observation in real cultures. We consider two scenarios for damage, targeted attacks on those neurons with the highest out-degree and random failures. By analyzing the evolution of both the giant connected component and the dynamical patterns of the neurons as nodes are removed, we observe that network activity halts for a removal of 50% of the nodes in targeted attacks, much lower than the 70% node removal required in the case of random failures. Notably, the decrease of neuronal activity is not gradual. Both damage scenarios portray "boosts" of activity just before full silencing that are not present in equivalent random (Erdös-Rényi) graphs. These boosts correspond to small, spatially compact subnetworks that are able to maintain high levels of activity. Since these subnetworks are absent in the equivalent random graphs, we hypothesize that metric correlations facilitate the existence of local circuits sufficiently integrated to maintain activity, shaping an intrinsic mechanism for resilience.
我们研究了空间神经元网络中损伤的结构和动力学后果。受真实体外网络的启发,我们构建了嵌入在二维空间中的有向网络,并遵循设计系统布线的生物学规则。结果,合成培养物显示出与真实实验中观察到的相似的强度量相关性。反过来,通过采用从真实培养物中观察到的参数采用 Izhikevich 模型来纳入神经元动力学。我们考虑了两种损伤情况,即针对具有最高出度的神经元的靶向攻击和随机故障。通过分析在去除节点时巨连通分量和神经元动态模式的演化,我们观察到在靶向攻击中,当去除 50%的节点时,网络活动停止,远低于随机故障情况下需要去除 70%的节点。值得注意的是,神经元活动的减少不是渐进的。在完全沉默之前,两种损伤情况都描绘了“活动激增”,而在等效的随机(Erdős-Rényi)图中则没有。这些激增对应于能够维持高活动水平的小的、空间上紧凑的子网。由于这些子网在等效的随机图中不存在,我们假设度量相关性有助于存在足够集成以维持活动的局部电路,从而形成内在的弹性机制。