Darbin Olivier, Eghbalnia Hamid R, Romeo Andrew, Montgomery Erwin B
Department of Neurosurgery, University South Alabama, 307 University Blvd, Mobile, AL, 36688, USA.
Department of Molecular Biology and Biophysics, University of Connecticut Health Center, 263 Farmington Avenue, Farmington, CT, 06030, USA.
Sci Rep. 2025 Jan 2;15(1):220. doi: 10.1038/s41598-024-83970-8.
Nonlinear responses of individual neurons are both experimentally established and considered fundamental for the functioning of neuronal circuitry. Consequently, one may envisage the collective dynamics of large networks of neurons exhibiting a large repertoire of nonlinear behaviors. However, an ongoing and central challenge in the modeling of neural dynamics involves the trade-off between tractability and biological realism. This is particularly important in exploring the range of possible dynamics of large networks. Our approach uses Gaussian white noise as a probe, thus capturing the full range of system responses and characteristics by using an approach inspired by the well-established Wiener - Volterra nonlinear system identification approach. We assess model behavior over a range of network architectures and noise stimulation rates and demonstrate non-monotonicity and nonlinearity as a system property. Perhaps surprisingly, our computational model suggests that recurrent systems of nonlinear neurons exhibit a range of complex behaviors that do not readily yield to linear modeling in every setting. Our results suggest that a linear interpretation of experimental data is likely to discount the critical importance of properties emerging from network architecture. The main contribution of this effort is to highlight the importance of the network's architecture operating on the nonlinear properties of individual neurons and the experimental probing approaches of the circuitry.
单个神经元的非线性反应已通过实验得到证实，并被认为是神经元回路功能的基础。因此，可以设想大量神经元网络的集体动力学表现出大量的非线性行为。然而，神经动力学建模中一个持续存在的核心挑战涉及可处理性与生物真实性之间的权衡。这在探索大型网络可能的动力学范围时尤为重要。我们的方法使用高斯白噪声作为探针，从而通过采用受成熟的维纳 - 沃尔泰拉非线性系统识别方法启发的方法来捕捉系统反应和特征的全范围。我们在一系列网络架构和噪声刺激率范围内评估模型行为，并证明非单调性和非线性是一种系统属性。也许令人惊讶的是，我们的计算模型表明，非线性神经元的递归系统表现出一系列复杂行为，在每种情况下都不容易用线性建模来解释。我们的结果表明，对实验数据的线性解释可能会忽视网络架构所产生属性的关键重要性。这项工作的主要贡献在于强调网络架构对单个神经元非线性属性的作用以及对回路的实验探测方法的重要性。
