论文标题
相互作用的非线性驱动神经振荡器的网络在强耦合时脱干
The nonlinearity of interactions drives networks of neural oscillators to decoherence at strong coupling
论文作者
论文摘要
尽管相位振荡器通常用于对神经元种群进行建模，但与库拉莫托范式相比，大脑区域之间的强相互作用可能与同步丧失有关。使用神经质量模型所描述的耦合振荡器网络，我们发现在耦合强度提高的耦合强度在基本非线性中的转变，例如，由节点之间的相互作用引起的基本非线性。非线性驱动的过渡也取决于连接拓扑，强调了网络结构在塑造大脑活动中的作用。
While phase oscillators are often used to model neuronal populations, in contrast to the Kuramoto paradigm, strong interactions between brain areas can be associated with loss of synchrony. Using networks of coupled oscillators described by neural mass models, we find that a transition to decoherence at increased coupling strength results from the fundamental nonlinearity, e.g., arising from refractoriness, of the interactions between the nodes. The nonlinearity-driven transition also depends on the connection topology, underlining the role of network structure in shaping brain activity.
