Working memory requires the brain to maintain information from the recent past to guide ongoing behavior. Neurons can contribute to this capacity by slowly integrating their inputs over time, creating persistent activity that outlasts the original stimulus. However, when these slowly integrating neurons are organized hierarchically, they introduce cumulative delays that create a fundamental challenge for learning: teaching signals that indicate whether behavior was correct or incorrect arrive out-of-sync with the neural activity they are meant to instruct. Here, we demonstrate that neurons enhanced with an adaptive current can compensate for these delays by responding to external stimuli prospectively -- effectively predicting future inputs to synchronize with them. First, we show that such prospective neurons enable teaching signal synchronization across a range of learning algorithms that propagate error signals through hierarchical networks. Second, we demonstrate that this successfully guides learning in slowly integrating neurons, enabling the formation and retrieval of memories over extended timescales. We support our findings with a mathematical analysis of the prospective coding mechanism and learning experiments on motor control tasks. Together, our results reveal how neural adaptation could solve a critical timing problem and enable efficient learning in dynamic environments.

翻译：工作记忆要求大脑维持近期信息以指导当前行为。神经元可通过随时间缓慢整合输入来贡献于此能力，产生持续于原始刺激的持久活动。然而，当这些缓慢整合的神经元按层级组织时，会引入累积延迟，从而造成学习的基本挑战：指示行为正确与否的教学信号与其本应指导的神经活动出现失同步。本文证明，具有自适应电流增强的神经元可通过前瞻性响应外部刺激来补偿这些延迟——即有效预测未来输入以实现同步。首先，我们展示此类前瞻性神经元能在多种通过层级网络传播误差信号的学习算法中实现教学信号同步。其次，我们证明这能成功指导缓慢整合神经元的学习，实现在延长时间尺度上形成与检索记忆。我们通过前瞻性编码机制的数学分析及运动控制任务的学习实验支持了这些发现。综合而言，我们的研究揭示了神经适应性如何解决关键时序问题，并实现动态环境中的高效学习。