We investigate the dynamic mechanisms underlying intermittent state transitions in a recently proposed neural mass model of epilepsy. A low dimensional model is constructed, which preserves two key features of the neural mass model, namely (i) coupling between oscillators and (ii) heterogeneous proximity of these oscillators to a bifurcation between distinct limit cycles. We demonstrate that state transitions due to intermittency occur in the abstract model. This suggests that there is a general bifurcation mechanism responsible for this behaviour and that this is independent of the precise form of the evolution equations. Such abstractions of neural mass models allow a deeper insight into underlying dynamic and physiological mechanisms, and also allow the more efficient exploration of large scale brain dynamics in disease.

中文翻译：

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癫痫耦合异质振荡器模型中间歇状态转换的机制。

我们研究了最近提出的癫痫神经质量模型中间歇性状态转换的动态机制。构建了一个低维模型，它保留了神经质量模型的两个关键特征，即 (i) 振荡器之间的耦合和 (ii) 这些振荡器与不同极限循环之间的分叉的异质接近度。我们证明了由于间歇性导致的状态转换发生在抽象模型中。这表明存在导致这种行为的一般分叉机制，并且这与演化方程的精确形式无关。神经质量模型的这种抽象允许更深入地了解潜在的动态和生理机制，并且还允许更有效地探索疾病中的大规模大脑动力学。