Department of Cognitive Sciences, University of California Irvine, CA, USA ; Department of Biomedical Engineering, University of California Irvine, CA, USA ; Institute for Mathematical Behavioral Sciences, University of California Irvine, CA, USA.
Front Comput Neurosci. 2013 Apr 18;7:29. doi: 10.3389/fncom.2013.00029. eCollection 2013.
The response of a population of cortical neurons to an external stimulus depends not only on the receptive field properties of the neurons, but also the level of arousal and attention or goal-oriented cognitive biases that guide information processing. These top-down effects on cortical neurons bias the output of the neurons and affect behavioral outcomes such as stimulus detection, discrimination, and response time. In any physiological study, neural dynamics are observed in a specific brain state; the background state partly determines neuronal excitability. Experimental studies in humans and animal models have also demonstrated that slow oscillations (typically in the alpha or theta bands) modulate the fast oscillations (gamma band) associated with local networks of neurons. Cross-frequency interaction is of interest as a mechanism for top-down or bottom up interactions between systems at different spatial scales. We develop a generic model of top-down influences on local networks appropriate for comparison with EEG. EEG provides excellent temporal resolution to investigate neuronal oscillations but is space-averaged on the cm scale. Thus, appropriate EEG models are developed in terms of population synaptic activity. We used the Wilson-Cowan population model to investigate fast (gamma band) oscillations generated by a local network of excitatory and inhibitory neurons. We modified the Wilson-Cowan equations to make them more physiologically realistic by explicitly incorporating background state variables into the model. We found that the population response is strongly influenced by the background state. We apply the model to reproduce the modulation of gamma rhythms by theta rhythms as has been observed in animal models and human ECoG and EEG studies. The concept of a dynamic background state presented here using the Wilson-Cowan model can be readily applied to incorporate top-down modulation in more detailed models of specific cortical systems.
皮层神经元对外部刺激的反应不仅取决于神经元的感受野特性，还取决于唤醒水平、注意力或导向目标的认知偏差，这些偏差指导着信息处理。这些自上而下的效应对皮层神经元产生影响，从而影响行为结果，如刺激检测、辨别和反应时间。在任何生理研究中，神经动力学都是在特定的大脑状态下观察到的；背景状态在一定程度上决定了神经元的兴奋性。人类和动物模型的实验研究也表明，慢波（通常在 alpha 或 theta 频段）调制与神经元局部网络相关的快波（gamma 频段）。跨频相互作用作为不同空间尺度系统之间自上而下或自下而上相互作用的机制引起了人们的兴趣。我们开发了一种适用于与 EEG 进行比较的局部网络自上而下影响的通用模型。EEG 提供了极好的时间分辨率来研究神经元振荡，但在 cm 尺度上进行空间平均。因此，根据群体突触活动开发了适当的 EEG 模型。我们使用 Wilson-Cowan 群体模型来研究由兴奋性和抑制性神经元局部网络产生的快（gamma 频段）振荡。我们修改了 Wilson-Cowan 方程，通过将背景状态变量明确纳入模型，使它们更符合生理现实。我们发现群体反应强烈受到背景状态的影响。我们应用该模型来再现动物模型和人类 ECoG 和 EEG 研究中观察到的 theta 节律对 gamma 节律的调制。这里使用 Wilson-Cowan 模型提出的动态背景状态概念可以很容易地应用于更详细的特定皮质系统模型中的自上而下调制。
