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 of prediction error minimization. Here we 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 estimate the precision of their predictions. This results in a dynamic modulatory balancing of cortical streams based on context-dependent precision estimates. Moreover, the theory predicts the existence of cortical second-order errors, comparing estimated and actual precision, propagated through the cortical hierarchy alongside classical prediction errors. These second-order errors are used to learn weights of synapses responsible for precision estimation through an error-correcting synaptic learning rule. Finally, we propose a detailed mapping of the theory to cortical circuitry.

翻译：皮层预测误差的最小化被认为是大脑皮层在感知、行动和学习过程中的关键计算目标。然而，目前尚不清楚皮层应如何在此预测误差最小化过程中形成并利用关于不确定性的信息。本文推导了在皮层区域不仅需要预测其他区域及感觉流中的活动，同时还需联合估计其预测精度的假设下，能够最小化预测误差的神经动力学机制。这一过程基于上下文依赖的精度估计，实现了皮层流中动态的调节性平衡。此外，该理论预测了皮层中二阶误差的存在——即估计精度与实际精度的比较——这些误差与经典预测误差一同沿着皮层层级传播。这些二阶误差通过一种纠错突触学习规则，用于学习负责精度估计的突触权重。最后，我们提出了该理论与皮层回路结构的详细映射关系。