Granier Arno, Petrovici Mihai A, Senn Walter, Wilmes Katharina A
Department of Physiology, University of Bern, Bühlplatz 5, Bern 3012, Switzerland.
Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland.
PNAS Nexus. 2024 Sep 13;3(9):pgae404. doi: 10.1093/pnasnexus/pgae404. eCollection 2024 Sep.
Minimization of cortical prediction errors has been considered a key computational goal of the cerebral cortex underlying perception, action, and learning. However, it is still unclear how the cortex should form and use information about uncertainty in this process. Here, we formally derive neural dynamics that minimize prediction errors under the assumption that cortical areas must not only predict the activity in other areas and sensory streams but also jointly project their confidence (inverse expected uncertainty) in their predictions. In the resulting neuronal dynamics, the integration of bottom-up and top-down cortical streams is dynamically modulated based on confidence in accordance with the Bayesian principle. Moreover, the theory predicts the existence of cortical second-order errors, comparing confidence and actual performance. These errors are propagated through the cortical hierarchy alongside classical prediction errors and are used to learn the weights of synapses responsible for formulating confidence. We propose a detailed mapping of the theory to cortical circuitry, discuss entailed functional interpretations, and provide potential directions for experimental work.
皮层预测误差的最小化被认为是大脑皮层在感知、行动和学习背后的关键计算目标。然而，目前仍不清楚皮层在这个过程中应该如何形成和使用关于不确定性的信息。在这里，我们正式推导神经动力学，在皮层区域不仅必须预测其他区域和感觉信息流中的活动，而且还必须共同投射它们对预测的置信度（逆期望不确定性）的假设下，使预测误差最小化。在由此产生的神经元动力学中，自下而上和自上而下的皮层信息流的整合根据贝叶斯原理，基于置信度进行动态调制。此外，该理论预测了皮层二阶误差的存在，即比较置信度和实际表现。这些误差与经典预测误差一起在皮层层级中传播，并用于学习负责制定置信度的突触权重。我们提出了该理论与皮层电路的详细映射，讨论了相关的功能解释，并为实验工作提供了潜在的方向。
