论文标题
下一代神经质量模型中的噪声
Shot noise in next-generation neural mass models
论文作者
论文摘要
最近，所谓的下一代神经质量模型在数学神经科学领域引起了很多研究人员的关注。这些模型可以说明神经种群同步程度的能力在许多情况下被证明是有用的，例如大脑节奏的建模，工作记忆和时空活动的时空模式。在本信中，我们研究有限大小对神经网络集体行为的影响，并表明它们可以通过适当修改的神经质量模型来捕获。也就是说，网络的有限大小导致射击噪声的出现在神经质量模型中作为随机术语出现。我们计算出射击噪声的功率谱，并表明它可能显示出与平均点火率相当的频率的明显峰值。尽管在大型连接网络中，射击噪声较弱，但由于共振效应，其对集体动力的影响可能至关重要。
Recently, the so-called next-generation neural mass models have received a lot of attention of the researchers in the field of mathematical neuroscience. The ability of these models to account for the degree of synchrony in neural populations proved useful in many contexts such as the modeling of brain rhythms, working memory and spatio-temporal patterns of activity. In the present Letter we study the effects of finite size on the collective behaviour of neural networks and show that they can be captured by appropriately modified neural mass models. Namely, the finite size of the network leads to the emergence of the shot noise appearing as a stochastic term in the neural mass model. We calculate the power spectrum of the shot noise and show that it might demonstrate pronounced peaks in the frequencies comparable to the mean firing rate. Although the shot noise is weak in large massively connected networks, its impact on the collective dynamics might be crucial due to resonance effects.
