我们的大脑不断产生与实际输入相比较的感觉输入的预测,通过大脑区域的层次结构传播预测误差,并随后更新世界的内部预测。然而,预测编码的基本特征,分层深度的概念及其神经机制,在很大程度上仍未探索。这里,我们结合功能性磁共振成像(fMRI)和高密度全脑皮质图(ECoG),研究了在听觉局部-全局范式中预测听觉处理的分层深度,在该范式中,刺激的时间规律被设计为两个分层水平.将预测误差和预测更新作为对听觉不匹配和遗漏的神经反应进行检查。使用功能磁共振成像,我们确定了沿听觉通路的分层梯度:中脑和感觉区域代表局部,较短时间尺度的预测处理,然后是联想听觉区域,而颞前区和前额区代表全球,更长时间尺度的序列处理。互补的ECoG记录证实了皮质表面区域的激活,并进一步区分了预测误差和更新的信号,通过推定的自下而上的γ和自上而下的β振荡传输,分别。此外,由于缺乏输入而引起的遗漏响应,仅反映分层预测编码框架特有的两级预测信号,展示了听觉中自上而下的分层预测过程,temporal,和前额区。因此,我们的发现支持分层预测编码框架,并概述了如何使用神经网络和时空动力学来表示和排列Marmoset大脑中听觉序列的层次结构。
Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in
marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, shorter-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, longer-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putative bottom-up γ and top-down β oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical top-down process of predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural networks and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the
marmoset brain.