Nonlinear modeling of dynamic interactions within neuronal ensembles using Principal Dynamic Modes

Vasilis Z. Marmarelis; Dae C. Shin; Dong Song; Robert E. Hampson; Sam A. Deadwyler; Theodore W. Berger

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Nonlinear modeling of dynamic interactions within neuronal ensembles using Principal Dynamic Modes

机译：使用主要动态模式对神经元集合内的动态相互作用进行非线性建模

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摘要

A methodology for nonlinear modeling of multi-input multi-output (MIMO) neuronal systems is presented that utilizes the concept of Principal Dynamic Modes (PDM). The efficacy of this new methodology is demonstrated in the study of the dynamic interactions between neuronal ensembles in the Pre-Frontal Cortex (PFC) of a behaving non-human primate (NHP) performing a Delayed Match-to-Sample task. Recorded spike trains from Layer-2 and Layer-5 neurons were viewed as the "inputs" and "outputs", respectively, of a putative MIMO system/model that quantifies the dynamic transformation of multi-unit neuronal activity between Layer-2 and Layer-5 of the PFC. Model prediction performance was evaluated by means of computed Receiver Operating Characteristic (ROC) curves. The PDM-based approach seeks to reduce the complexity of MIMO models of neuronal ensembles in order to enable the practicable modeling of large-scale neural systems incorporating hundreds or thousands of neurons, which is emerging as a preeminent issue in the study of neural function. The "scaling-up" issue has attained critical importance as multi-electrode recordings are increasingly used to probe neural systems and advance our understanding of integrated neural function. The initial results indicate that the PDM-based modeling methodology may greatly reduce the complexity of the MIMO model without significant degradation of performance. Furthermore, the PDM-based approach offers the prospect of improved biological/physiological interpretation of the obtained MIMO models.

机译：提出了一种利用主动态模式（PDM）概念对多输入多输出（MIMO）神经元系统进行非线性建模的方法。这项新方法的功效在行为非人类灵长类动物（NHP）的前额叶皮层（PFC）中神经元集成体之间的动态相互作用的研究中得到了证明，该行为执行了延迟的匹配样本任务。来自第2层和第5层神经元的记录的尖峰序列分别被视为推定的MIMO系统/模型的“输入”和“输出”，该MIMO系统/模型量化了第2层和第3层之间的多单元神经元活动的动态转换。 -5的PFC。模型预测性能通过计算的接收器工作特性（ROC）曲线进行评估。基于PDM的方法旨在降低神经元集成体的MIMO模型的复杂性，以便对包含成百上千个神经元的大规模神经系统进行切实可行的建模，这在神经功能研究中正在成为一个突出的问题。随着越来越多的多电极记录被用于探测神经系统并加深我们对集成神经功能的理解，“放大”问题已变得至关重要。初始结果表明，基于PDM的建模方法可以大大降低MIMO模型的复杂度，而不会显着降低性能。此外，基于PDM的方法为获得的MIMO模型提供了改进的生物学/生理学解释的前景。

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来源
《Journal of Computational Neuroscience》 |2013年第1期|73-87|共15页
作者
Vasilis Z. Marmarelis; Dae C. Shin; Dong Song; Robert E. Hampson; Sam A. Deadwyler; Theodore W. Berger;
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作者单位

University of Southern California, Los Angeles, CA, USA;

University of Southern California, Los Angeles, CA, USA;

University of Southern California, Los Angeles, CA, USA;

Wake Forest University, Winston-Salem, NC 27157-1083, USA;

Wake Forest University, Winston-Salem, NC 27157-1083, USA;

University of Southern California, Los Angeles, CA, USA;

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正文语种 eng
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关键词
multi-input multi-output neuronal systems; pre- frontal cortex; dynamic modeling -nonlinear modeling; principal dynamic modes; volterra modeling;

机译：多输入多输出神经元系统;前额叶皮层动态建模-非线性建模;主要动态模式;沃尔泰拉建模;

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Nonlinear modeling of dynamic interactions within neuronal ensembles using Principal Dynamic Modes

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