一种用于诱发痛觉和自发痛觉感知的预测编码模型。
A Predictive Coding Model for Evoked and Spontaneous Pain Perception.
作者信息
Song Yuru, Kemprecos Helen, Wang Jing, Chen Zhe
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:2964-2967. doi: 10.1109/EMBC.2019.8857298.
Abstract
Pain is a complex multidimensional experience, and pain perception is still incompletely understood. Here we combine animal behavior, electrophysiology, and computer modeling to dissect mechanisms of evoked and spontaneous pain. We record the local field potentials (LFPs) from the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC) of freely behaving rats during pain episodes, and develop a predictive coding model to investigate the temporal coordination of oscillatory activity between the S1 and ACC. Our preliminary results from computational simulations support the experimental findings and provide new predictions.
摘要
疼痛是一种复杂的多维度体验，而对疼痛感知的理解仍不完整。在此，我们结合动物行为、电生理学和计算机建模来剖析诱发痛和自发痛的机制。我们在疼痛发作期间记录自由活动大鼠的初级体感皮层（S1）和前扣带皮层（ACC）的局部场电位（LFP），并开发一种预测编码模型来研究S1和ACC之间振荡活动的时间协调性。我们计算模拟的初步结果支持了实验发现并提供了新的预测。
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