Feedback-rich neural architectures can regenerate earlier representations and inject temporal context, making them a natural setting for strictly local synaptic plasticity. Existing literature raises doubt about whether a minimal, backpropagation-free feedback-Hebbian system can already express interpretable continual-learning-relevant behaviors under controlled training schedules. In this work, we introduce a compact prediction-reconstruction architecture with a dedicated feedback pathway providing lightweight, locally trainable temporal context for continual adaptation. All synapses are updated by a unified local rule combining centered Hebbian covariance, Oja-style stabilization, and a local supervised drive where targets are available. With a simple two-pair association task, learning is characterized through layer-wise activity snapshots, connectivity trajectories (row and column means of learned weights), and a normalized retention index across phases. Under sequential A to B training, forward output connectivity exhibits a long-term depression (LTD)-like suppression of the earlier association, while feedback connectivity preserves an A-related trace during acquisition of B. Under an alternating sequence, both associations are concurrently maintained rather than sequentially suppressed. Architectural controls and rule-term ablations isolate the role of dedicated feedback in regeneration and co-maintenance, alongside the role of the local supervised term in output selectivity and unlearning. Together, the results show that a compact feedback pathway trained with local plasticity can support regeneration and continual-learning-relevant dynamics in a minimal, mechanistically transparent setting.

翻译：反馈丰富的神经架构能够再生早期表征并注入时间上下文，这使其成为严格局部突触可塑性的自然设置。现有文献对一个最小化的、无反向传播的反馈-赫布系统是否能在受控训练计划下已能表达可解释的持续学习相关行为提出了质疑。在本工作中，我们引入了一种紧凑的预测-重构架构，其具有一个专用的反馈通路，为持续适应提供轻量级、可局部训练的时间上下文。所有突触均通过一个统一的局部规则进行更新，该规则结合了中心化赫布协方差、Oja式稳定化以及目标可用时的局部监督驱动。通过一个简单的双对关联任务，学习过程通过逐层活动快照、连接轨迹（学习权重的行与列均值）以及跨阶段的标准化保持指数进行表征。在从A到B的顺序训练下，前向输出连接表现出对早期关联的长时程抑制（LTD）样抑制，而反馈连接在获取B期间保留了与A相关的痕迹。在交替序列下，两个关联被同时维持而非顺序抑制。架构控制和规则项消融实验分离了专用反馈通路在再生和共同维持中的作用，以及局部监督项在输出选择性和遗忘中的作用。综合来看，结果表明，一个通过局部可塑性训练的紧凑反馈通路能够在一个最小化、机制透明的设置中支持再生和持续学习相关的动态。