Piccinini Juan, Ipiñna Ignacio Perez, Laufs Helmut, Kringelbach Morten, Deco Gustavo, Sanz Perl Yonatan, Tagliazucchi Enzo
Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires 1428, Argentina.
Neurology Department, University of Kiel, Kiel 24105, Germany.
Chaos. 2021 Feb;31(2):023127. doi: 10.1063/5.0025543.
An outstanding open problem in neuroscience is to understand how neural systems are capable of producing and sustaining complex spatiotemporal dynamics. Computational models that combine local dynamics with in vivo measurements of anatomical and functional connectivity can be used to test potential mechanisms underlying this complexity. We compared two conceptually different mechanisms: noise-driven switching between equilibrium solutions (modeled by coupled Stuart-Landau oscillators) and deterministic chaos (modeled by coupled Rossler oscillators). We found that both models struggled to simultaneously reproduce multiple observables computed from the empirical data. This issue was especially manifested in the case of noise-driven dynamics close to a bifurcation, which imposed overly strong constraints on the optimal model parameters. In contrast, the chaotic model could produce complex behavior over a range of parameters, thus being capable of capturing multiple observables at the same time with good performance. Our observations support the view of the brain as a non-equilibrium system able to produce endogenous variability. We presented a simple model capable of jointly reproducing functional connectivity computed at different temporal scales. Besides adding to our conceptual understanding of brain complexity, our results inform and constrain the future development of biophysically realistic large-scale models.
神经科学中一个突出的开放性问题是理解神经系统如何能够产生并维持复杂的时空动态。将局部动态与解剖和功能连接性的体内测量相结合的计算模型，可用于测试这种复杂性背后的潜在机制。我们比较了两种概念上不同的机制：平衡解之间的噪声驱动切换（由耦合的斯图尔特 - 兰道振荡器建模）和确定性混沌（由耦合的罗斯勒振荡器建模）。我们发现，这两种模型都难以同时重现从经验数据计算出的多个可观测值。这个问题在接近分岔的噪声驱动动态情况下尤为明显，这对最优模型参数施加了过强的约束。相比之下，混沌模型能够在一系列参数范围内产生复杂行为，因此能够同时以良好的性能捕捉多个可观测值。我们的观察结果支持将大脑视为能够产生内源性变异性的非平衡系统的观点。我们提出了一个能够联合重现不同时间尺度上计算出的功能连接性的简单模型。除了增加我们对大脑复杂性的概念理解之外，我们的结果还为生物物理现实的大规模模型的未来发展提供了信息并加以限制。
