Tsubo Yasuhiro, Shinomoto Shigeru
College of Information Science and Engineering, Ritsumeikan University, Osaka 567-8570, Japan.
Research Organization of Open Innovation and Collaboration, Ritsumeikan University, Osaka 567-8570, Japan.
PNAS Nexus. 2024 Jul 1;3(7):pgae261. doi: 10.1093/pnasnexus/pgae261. eCollection 2024 Jul.
Spike raster plots of numerous neurons show vertical stripes, indicating that neurons exhibit synchronous activity in the brain. We seek to determine whether these coherent dynamics are caused by smooth brainwave activity or by something else. By analyzing biological data, we find that their cross-correlograms exhibit not only slow undulation but also a cusp at the origin, in addition to possible signs of monosynaptic connectivity. Here we show that undulation emerges if neurons are subject to smooth brainwave oscillations while a cusp results from nondifferentiable fluctuations. While modern analysis methods have achieved good connectivity estimation by adapting the models to slow undulation, they still make false inferences due to the cusp. We devise a new analysis method that may solve both problems. We also demonstrate that oscillations and nondifferentiable fluctuations may emerge in simulations of large-scale neural networks.
众多神经元的峰电位光栅图显示出垂直条纹，这表明神经元在大脑中表现出同步活动。我们试图确定这些相干动力学是由平滑的脑电波活动还是其他因素引起的。通过分析生物学数据，我们发现它们的互相关图不仅呈现出缓慢的波动，而且在原点处有一个尖点，此外还有可能存在单突触连接的迹象。在这里我们表明，如果神经元受到平滑的脑电波振荡影响，就会出现波动，而尖点则是由不可微波动导致的。虽然现代分析方法通过使模型适应缓慢波动已经实现了良好的连接性估计，但由于尖点的存在，它们仍然会做出错误的推断。我们设计了一种新的分析方法，可能会解决这两个问题。我们还证明了在大规模神经网络的模拟中可能会出现振荡和不可微波动。
