Laxminarayan Srinivas, Tadmor Gilead, Diamond Solomon G, Miller Eric, Franceschini Maria Angela, Brooks Dana H
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA.
Biol Cybern. 2011 Dec;105(5-6):371-97. doi: 10.1007/s00422-012-0472-z. Epub 2012 Jan 27.
Habituation is a generic property of the neural response to repeated stimuli. Its strength often increases as inter-stimuli relaxation periods decrease. We propose a simple, broadly applicable control structure that enables a neural mass model of the evoked EEG response to exhibit habituated behavior. A key motivation for this investigation is the ongoing effort to develop model-based reconstruction of multi-modal functional neuroimaging data. The control structure proposed here is illustrated and validated in the context of a biophysical neural mass model, developed by Riera et al. (Hum Brain Mapp 27(11):896-914, 2006; 28(4):335-354, 2007), and of simplifications thereof, using data from rat EEG response to medial nerve stimuli presented at frequencies from 1 to 8 Hz. Performance was tested by predictions of both the response to the next stimulus based on the current one, and also of continued stimuli trains over 4-s time intervals based on the first stimulus in the interval, with similar success statistics. These tests demonstrate the ability of simple generative models to capture key features of the evoked response, including habituation.
习惯化是神经对重复刺激反应的一种普遍特性。其强度通常会随着刺激间隔松弛期的缩短而增强。我们提出了一种简单且广泛适用的控制结构，该结构能使诱发脑电图反应的神经团模型表现出习惯化行为。这项研究的一个关键动机是当前正在进行的基于模型重建多模态功能神经成像数据的工作。这里提出的控制结构在Riera等人（《人类大脑图谱》27(11):896 - 914, 2006；28(4):335 - 354, 2007）开发的生物物理神经团模型及其简化模型的背景下进行了说明和验证，使用了大鼠对频率为1至8赫兹的正中神经刺激的脑电图反应数据。通过基于当前刺激预测对下一个刺激的反应，以及基于间隔中的第一个刺激预测4秒时间间隔内的连续刺激序列，对性能进行了测试，得到了相似的成功统计结果。这些测试证明了简单生成模型捕捉诱发反应关键特征（包括习惯化）的能力。
