耦合对神经元极端事件的影响:缓解和增强

A. Roy, S. Sinha
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引用次数: 0

摘要

我们重点研究了局部扩散耦合和全局平均场耦合下非周期神经元图集合中极端事件的出现。我们的主要发现是局部扩散耦合提高了时空极端事件发生的概率,而与之形成鲜明对比的是,全局平均场耦合抑制了极端事件的发生。因此,耦合的性质至关重要地决定了极端事件是通过耦合增强还是减轻。此外,在全局耦合系统中,耦合强度窗口中存在表现空间极端事件的初始状态,但不表现时间极端事件,这表明空间极端事件并不意味着时间极端事件。我们还探讨了在连续事件间间隔的回归图中存在的可识别模式,以衡量短期风险评估。我们发现单神经元图,以及强扩散耦合下的系统,在这些返回图中显示出广泛的噪声模式,在特征区间周围有集群,允许在极端事件序列中有一些短期的可预测性。相反,在弱扩散耦合和全局耦合下,事件间区间失去了所有可感知的相关性,并且分布扩展到非常大的事件间区间。最后,我们研究了一种非局部扩散耦合形式。有趣的是,这种耦合产生了一个时间极端事件发生的大窗口,但空间剖面是同步的,即我们发现了同步的时间极端事件。这种同步的极端尖峰使人联想到导致癫痫发作的神经元活动,并可能与大脑活动中的极端事件相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of coupling on neuronal extreme events: Mitigation and enhancement
We focus on the emergence of extreme events in a collection of aperiodic neuronal maps, under local diffusive coupling, as well as global mean-field coupling. Our central finding is that local diffusive coupling enhances the probability of occurrence of both temporal and spatial extreme events, while in marked contrast, global mean-field coupling suppresses extreme events. So the nature of the coupling crucially determines whether the extreme events are enhanced or mitigated by coupling. Further, in globally coupled systems, there exist initial states in a window of coupling strength that exhibit spatial extreme events, but not temporal extreme events, suggesting that spatial extreme events do not imply temporal extreme events. We also explored the existence of discernible patterns in the return maps of successive inter-event intervals in order to gauge short-term risk-assessment. We find that single neuronal maps, as well as systems under strong diffusive coupling, display broad noisy patterns in these return maps, with clusters around characteristic intervals, allowing some short-term predictability in the extreme event sequence. In contrast, under weak diffusive coupling and global coupling, inter-event intervals lose all perceptible correlations, and the distribution extends to very large inter-event intervals. Lastly, we investigated a non-local diffusive coupling form. Interestingly, this coupling yielded a large window where temporal extreme events occurred, but the spatial profile was synchronized, namely, we found synchronized temporal extreme events. Such synchronized extreme spiking is reminiscent of the neuronal activity leading to epileptic seizures and is of potential relevance to extreme events in brain activity.
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