Abstract
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.
Highlights
The mammalian brain is organized as a functional hierarchy, through which information propagates from the lower levels of sensory or motor regions to the higher levels (Fuster, 1997; Huntenburg et al, 2018; Mesulam, 1998; Parras et al, 2017)
(comprising 20% of trials) (Figure 1B; for details, see Materials and Methods). We used this paradigm to test the hypothesis of hierarchical neural dynamics during auditory sequence processing (Figure 1C), which predicts how the brain informs estimated statistics on the basis of the history of auditory sequences at two levels
1) At the 1st (“local”) level, expectation violations result from tone-to-tone transition probability, which uses the most recent (150 ms) observations only, e.g., the perception of an oddball (Y) tone deviating from four repetitions of standard (x) tones
Summary
The mammalian brain is organized as a functional hierarchy, through which information propagates from the lower levels of sensory or motor regions (including subcortical areas) to the higher levels (e.g., temporal and prefrontal areas) (Fuster, 1997; Huntenburg et al, 2018; Mesulam, 1998; Parras et al, 2017). The hierarchical predictive coding theory offers a unified framework for this functional hierarchy It states that the brain develops a generative model of the world that constantly predicts sensory input (Bizley & Cohen, 2013; Rao & Ballard, 1999; Spratling, 2010). The comparison of predicted and actual sensory input updates an internal representation of the world (Keller & Mrsic-Flogel, 2018). This process occurs throughout the cortical hierarchy. The key assumption of the theory is that the prediction-error propagates across different depths of hierarchy, and in turn, the prediction propagates backward, providing signals to update the internal model at each level. The essential feature of predictive coding, i.e., the notion of hierarchical depth (across multiple levels), is less well investigated (Chao et al, 2018; Parras et al, 2017)
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