标题： 海布记忆增强型循环网络：深度学习中的痕迹神经元 显示英文标题

标题： Hebbian Memory-Augmented Recurrent Networks: Engram Neurons in Deep Learning

摘要： 尽管在各种任务中取得了成功，当前的人工循环网络架构主要依赖于隐式的隐藏状态记忆，这限制了它们的可解释性以及建模长距离依赖关系的能力。 相比之下，生物神经系统使用显式的、关联的记忆痕迹（即，印迹）通过赫布突触可塑性得到加强，并在回忆期间稀疏激活。 受这些神经生物学见解的启发，我们引入了印迹神经网络（ENN），这是一种新的循环架构，结合了显式的、可微分的记忆矩阵，具有赫布可塑性和稀疏的、注意力驱动的检索机制。 ENN通过动态赫布痕迹显式地建模记忆形成和回忆，与传统RNN变体相比，提高了透明度和可解释性。 我们在三个经典基准上评估了ENN架构：MNIST数字分类、CIFAR-10图像序列建模和WikiText-103语言建模。 我们的实证结果表明，ENN在准确性和泛化性能方面与传统的RNN、GRU和LSTM架构相当，所有模型在大规模WikiText-103任务上收敛到相似的准确率和困惑度。 同时，ENN通过可观测的记忆动态显著提高了可解释性。 赫布痕迹可视化进一步揭示了生物上合理的、结构化的记忆形成过程，验证了受神经科学启发的机制在指导更可解释和稳健的深度学习模型发展方面的潜力。 显示英文摘要

摘要： Despite success across diverse tasks, current artificial recurrent network architectures rely primarily on implicit hidden-state memories, limiting their interpretability and ability to model long-range dependencies. In contrast, biological neural systems employ explicit, associative memory traces (i.e., engrams) strengthened through Hebbian synaptic plasticity and activated sparsely during recall. Motivated by these neurobiological insights, we introduce the Engram Neural Network (ENN), a novel recurrent architecture incorporating an explicit, differentiable memory matrix with Hebbian plasticity and sparse, attention-driven retrieval mechanisms. The ENN explicitly models memory formation and recall through dynamic Hebbian traces, improving transparency and interpretability compared to conventional RNN variants. We evaluate the ENN architecture on three canonical benchmarks: MNIST digit classification, CIFAR-10 image sequence modeling, and WikiText-103 language modeling. Our empirical results demonstrate that the ENN achieves accuracy and generalization performance broadly comparable to classical RNN, GRU, and LSTM architectures, with all models converging to similar accuracy and perplexity on the large-scale WikiText-103 task. At the same time, the ENN offers significant enhancements in interpretability through observable memory dynamics. Hebbian trace visualizations further reveal biologically plausible, structured memory formation processes, validating the potential of neuroscience-inspired mechanisms to inform the development of more interpretable and robust deep learning models.