Department of Physics, University of Ottawa, 150 Louis Pasteur, Ottawa, Ontario K1N 6N5, Canada.
Chaos. 2021 Jan;31(1):013117. doi: 10.1063/5.0022350.
Many healthy and pathological brain rhythms, including beta and gamma rhythms and essential tremor, are suspected to be induced by noise. This yields randomly occurring, brief epochs of higher amplitude oscillatory activity known as "bursts," the statistics of which are important for proper neural function. Here, we consider a more realistic model with both multiplicative and additive noise instead of only additive noise, to understand how state-dependent fluctuations further affect rhythm induction. For illustrative purposes, we calibrate the model at the lower end of the beta band that relates to movement; parameter tuning can extend the relevance of our analysis to the higher frequency gamma band or to lower frequency essential tremors. A stochastic Wilson-Cowan model for reciprocally as well as self-coupled excitatory (E) and inhibitory (I) populations is analyzed in the parameter regime where the noise-free dynamics spiral in to a fixed point. Noisy oscillations known as quasi-cycles are then generated by stochastic synaptic inputs. The corresponding dynamics of E and I local field potentials can be studied using linear stochastic differential equations subject to both additive and multiplicative noises. As the prevalence of bursts is proportional to the slow envelope of the E and I firing activities, we perform an envelope-phase decomposition using the stochastic averaging method. The resulting envelope dynamics are uni-directionally coupled to the phase dynamics as in the case of additive noise alone but both dynamics involve new noise-dependent terms. We derive the stationary probability and compute power spectral densities of envelope fluctuations. We find that multiplicative noise can enhance network synchronization by reducing the magnitude of the negative real part of the complex conjugate eigenvalues. Higher noise can lead to a "virtual limit cycle," where the deterministically stable eigenvalues around the fixed point acquire a positive real part, making the system act more like a noisy limit cycle rather than a quasi-cycle. Multiplicative noise can thus exacerbate synchronization and possibly contribute to the onset of symptoms in certain motor diseases.
许多健康和病理的大脑节律,包括β和γ节律和特发性震颤,被怀疑是由噪声引起的。这会产生随机发生的、短暂的高振幅振荡活动,称为“爆发”,其统计特性对于正常的神经功能非常重要。在这里,我们考虑了一个更现实的模型,其中既有乘法噪声又有加法噪声,而不仅仅是加法噪声,以了解状态相关的波动如何进一步影响节律的诱导。为了说明问题,我们在与运动相关的β波段的低端对模型进行了校准;参数调整可以将我们的分析扩展到更高频率的γ波段或更低频率的特发性震颤。一个互惠的和自耦合的兴奋性(E)和抑制性(I)群体的随机威尔逊-考恩模型在无噪声动力学螺旋到一个固定点的参数范围内进行分析。然后通过随机突触输入产生称为准周期的噪声振荡。E 和 I 局部场电位的相应动力学可以使用受加法和乘法噪声影响的线性随机微分方程来研究。由于爆发的普遍性与 E 和 I 放电活动的慢包络成正比,因此我们使用随机平均方法进行包络-相位分解。所得的包络动力学与相位动力学单向耦合,就像只有加法噪声的情况一样,但两个动力学都涉及新的依赖噪声的项。我们导出了稳态概率,并计算了包络波动的功率谱密度。我们发现,乘法噪声可以通过减小复共轭特征值的负实部的大小来增强网络同步。更高的噪声可能导致“虚拟极限环”,其中固定点周围的确定性稳定特征值获得正实部,使系统的行为更像是噪声极限环而不是准周期。因此,乘法噪声可以加剧同步,并可能导致某些运动疾病的症状发作。