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Large-Scale Cortical Networks for Hierarchical Prediction and Prediction Error in the Primate Brain

31 Pages Posted: 3 Jun 2018 Sneak Peek Status: Published

See all articles by Zenas C. Chao

Zenas C. Chao

RIKEN Center for Brain Science; Kyoto University - Division of Neurobiology and Physiology

Kana Takaura

RIKEN Center for Brain Science

Liping Wang

Chinese Academy of Sciences (CAS) - Institute of Neuroscience

Naotaka Fujii

RIKEN Center for Brain Science

Stanislas Dehaene

Université Paris-Sud - Cognitive Neuroimaging Unit; Université Paris VI Pierre et Marie Curie - College de France

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Abstract

The predictive-coding theory proposes that cortical areas continuously generate and update predictions of sensory inputs, and emit predictionerror signals when the predicted and actual sensory inputs differ. According to the theory, the computations of predictions and prediction errors are carried out by hierarchically organized neuronal populations and transmitted between hierarchies via specific frequency channels. However, these multilevel processes are simultaneous and interdependent, making it difficult to disentangle their constituent neural network organization. Here, we test the theory by using hemisphere-wide, high-density electrocorticography (ECoG) to provide a large-scale characterization of the cortical networks for hierarchical auditory prediction and prediction-error processing in macaque monkeys. Broadband neuronal signals were collected during an auditory “local-global” paradigm in which the temporal regularities of the stimuli and their violations were controlled at two hierarchical levels. Using an automatized decomposition method for cortical activations and corticocortical interactions, we identified three distinct structures in the violation responses and further evaluated their functional interactions within and across trials. Structure 1, representing bottom-up processing of lower-level prediction errors, was reflected in γ oscillations (>40Hz) in the auditory cortex. Structure 2, representing the subsequent bottom-up processing of higher-level prediction errors, was reflected in γ oscillations in the anterior temporal cortex. Lastly, structure 3, representing a top-down updating of those predictions, was reflected in α/β-band interactions (<30Hz) from the prefrontal cortex back to the temporal cortex. Our findings provide strong support for hierarchical predictive coding and outline how it is dynamically implemented in signals using distinct areas and frequency bands.

Keywords: Auditory Processing, Predictive Coding, Prediction Error, Hierarchy, Large-Scale Cortical Network, Electrocorticography

Suggested Citation

Chao, Zenas C. and Takaura, Kana and Wang, Liping and Fujii, Naotaka and Dehaene, Stanislas, Large-Scale Cortical Networks for Hierarchical Prediction and Prediction Error in the Primate Brain (2018). Available at SSRN: https://ssrn.com/abstract=3188377 or http://dx.doi.org/10.2139/ssrn.3188377
This is a paper under consideration at Cell Press and has not been peer-reviewed.

Zenas C. Chao (Contact Author)

RIKEN Center for Brain Science ( email )

2-1 Hirosawa
Wako, Saitama 351-0198
Japan

Kyoto University - Division of Neurobiology and Physiology ( email )

Kyoto
Japan

Kana Takaura

RIKEN Center for Brain Science

2-1 Hirosawa
Wako, Saitama 351-0198
Japan

Liping Wang

Chinese Academy of Sciences (CAS) - Institute of Neuroscience

Shanghai
China

Naotaka Fujii

RIKEN Center for Brain Science

2-1 Hirosawa
Wako, Saitama 351-0198
Japan

Stanislas Dehaene

Université Paris-Sud - Cognitive Neuroimaging Unit

Paris
France

Université Paris VI Pierre et Marie Curie - College de France

11 Place Marcellin Berthelot
Paris, 75005
France

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