Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.
Nat Commun. 2024 Nov 8;15(1):9670. doi: 10.1038/s41467-024-54032-4.
Predictive coding theories propose that the brain constantly updates internal models to minimize prediction errors and optimize sensory processing. However, the neural mechanisms that link prediction error encoding and optimization of sensory representations remain unclear. Here, we provide evidence how predictive learning shapes the representational geometry of the human brain. We recorded magnetoencephalography (MEG) in humans listening to acoustic sequences with different levels of regularity. We found that the brain aligns its representational geometry to match the statistical structure of the sensory inputs, by clustering temporally contiguous and predictable stimuli. Crucially, the magnitude of this representational shift correlates with the synergistic encoding of prediction errors in a network of high-level and sensory areas. Our findings suggest that, in response to the statistical regularities of the environment, large-scale neural interactions engaged in predictive processing modulate the representational content of sensory areas to enhance sensory processing.
预测编码理论提出，大脑不断更新内部模型以最小化预测误差并优化感官处理。然而，将预测误差编码与感官表示的优化联系起来的神经机制尚不清楚。在这里，我们提供了证据表明预测学习如何塑造人类大脑的代表性几何形状。我们在人类听具有不同规则性水平的声序列时记录了脑磁图（MEG）。我们发现，大脑通过聚类时间上连续且可预测的刺激，将其代表性几何形状与感官输入的统计结构对齐。至关重要的是，这种代表性转变的幅度与高级和感官区域网络中协同编码预测误差相关。我们的研究结果表明，为了响应环境的统计规律，参与预测处理的大规模神经相互作用调节了感官区域的代表性内容以增强感官处理。
