Geng Kunling, Shin Dae C, Song Dong, Hampson Robert E, Deadwyler Samuel A, Berger Theodore W, Marmarelis Vasilis Z
Department of Biomedical Engineering and the Biomedical Simulations Resource Center at the University of Southern California, Los Angeles, CA, 90089, U.S.A.
Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, U.S.A.
Neural Comput. 2018 Jan;30(1):149-183. doi: 10.1162/neco_a_01031. Epub 2017 Oct 24.
This letter examines the results of input-output (nonparametric) modeling based on the analysis of data generated by a mechanism-based (parametric) model of CA3-CA1 neuronal connections in the hippocampus. The motivation is to obtain biological insight into the interpretation of such input-output (Volterra-equivalent) models estimated from synthetic data. The insights obtained may be subsequently used to interpretat input-output models extracted from actual experimental data. Specifically, we found that a simplified parametric model may serve as a useful tool to study the signal transformations in the hippocampal CA3-CA1 regions. Input-output modeling of model-based synthetic data show that GABAergic interneurons are responsible for regulating neuronal excitation, controlling the precision of spike timing, and maintaining network oscillations, in a manner consistent with previous studies. The input-output model obtained from real data exhibits intriguing similarities with its synthetic-data counterpart, demonstrating the importance of a dynamic resonance in the system/model response around 2 Hz to 3 Hz. Using the input-output model from real data as a guide, we may be able to amend the parametric model by incorporating more mechanisms in order to yield better-matching input-output model. The approach we present can also be applied to the study of other neural systems and pathways.
本信函基于对海马体中CA3-CA1神经元连接的基于机制（参数化）模型所生成数据的分析，研究了输入-输出（非参数化）建模的结果。其动机在于深入了解从合成数据估计出的此类输入-输出（等效于沃尔泰拉）模型的解释。随后获得的见解可用于解释从实际实验数据中提取的输入-输出模型。具体而言，我们发现一个简化的参数化模型可作为研究海马体CA3-CA1区域信号转换的有用工具。基于模型的合成数据的输入-输出建模表明，γ-氨基丁酸能中间神经元负责调节神经元兴奋、控制尖峰时间精度并维持网络振荡，这与先前的研究一致。从实际数据获得的输入-输出模型与其合成数据对应模型表现出有趣的相似性，表明系统/模型在2赫兹至3赫兹附近的响应中动态共振的重要性。以实际数据的输入-输出模型为指导，我们或许能够通过纳入更多机制来修正参数化模型，以产生匹配度更高的输入-输出模型。我们提出的方法也可应用于其他神经系统和通路的研究。
