Minimization of cortical prediction errors is believed to be a key canonical computation of the cerebral cortex underlying perception, action and learning. However, it is still unclear how the cortex should form and use knowledge about uncertainty in this process of prediction error minimization. Here we derive neural dynamics minimizing 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 leads to a dynamic modulatory balancing of cortical streams based on context-dependent precision estimates. Moreover, the theory predicts the existence of second-order prediction errors, i.e. errors on precision estimates, computed and 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 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执行感知、行动和学习的关键规范计算过程。然而，当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知识。本文推导了在皮层区域不仅需要预测其他区域及感觉信息流活动，还需联合估计其预测精度的假设下，实现预测误差最小化的神经动力学过程。这导致了基于情境依赖性精度估计的皮层信息流动态调制平衡。此外，该理论预测了二阶预测误差的存在——即关于精度估计的误差——这些误差与经典预测误差共同在皮层层级中计算并传播。此类二阶误差通过纠错型突触学习规则，用于调节负责精度估计的突触权重。最终，我们提出了该理论在皮层回路中的神经映射方案。