We propose HiCL, a novel hippocampal-inspired dual-memory continual learning architecture designed to mitigate catastrophic forgetting by using elements inspired by the hippocampal circuitry. Our system encodes inputs through a grid-cell-like layer, followed by sparse pattern separation using a dentate gyrus-inspired module with top-k sparsity. Episodic memory traces are maintained in a CA3-like autoassociative memory. Task-specific processing is dynamically managed via a DG-gated mixture-of-experts mechanism, wherein inputs are routed to experts based on cosine similarity between their normalized sparse DG representations and learned task-specific DG prototypes computed through online exponential moving averages. This biologically grounded yet mathematically principled gating strategy enables differentiable, scalable task-routing without relying on a separate gating network, and enhances the model's adaptability and efficiency in learning multiple sequential tasks. Cortical outputs are consolidated using Elastic Weight Consolidation weighted by inter-task similarity. Crucially, we incorporate prioritized replay of stored patterns to reinforce essential past experiences. Evaluations on standard continual learning benchmarks demonstrate the effectiveness of our architecture in reducing task interference, achieving near state-of-the-art results in continual learning tasks at lower computational costs. Our code is available here https://github.com/kushalk173-sc/HiCL.

翻译：我们提出HiCL，一种新颖的受海马体启发的双记忆持续学习架构，旨在通过借鉴海马体神经环路的原理来缓解灾难性遗忘。该系统通过类网格细胞层对输入进行编码，随后使用受齿状回启发的模块（采用top-k稀疏性）进行稀疏模式分离。情景记忆痕迹被保存在一个类CA3的自联想记忆中。任务特异性处理通过一种DG门控的专家混合机制进行动态管理：输入根据其归一化稀疏DG表示与通过在线指数移动平均计算得到的任务特异性DG原型之间的余弦相似度，被路由至相应的专家。这种基于生物学原理且具有数学严谨性的门控策略实现了可微分、可扩展的任务路由，无需依赖单独的门控网络，并增强了模型在连续学习多个任务时的适应性与效率。皮层输出通过基于任务间相似度加权的弹性权重巩固进行整合。关键的是，我们引入了存储模式的优先回放机制以强化重要的过往经验。在标准持续学习基准测试上的评估表明，我们的架构能有效减少任务干扰，以较低的计算成本在持续学习任务中取得了接近最先进水平的性能。我们的代码可在 https://github.com/kushalk173-sc/HiCL 获取。