Engram Memory Network：借鉴人脑记忆机制的原型可解释模型
Engram Memory Network: Brain-Inspired Prototype Explanations
一篇可解释 AI 论文，EMN 模仿人脑记忆检索原型来解释分类结果，在四个数据集上把子类对齐提升了 104.57%，准确率也没落下。
论文提出 Engram Memory Network（EMN），模拟人脑腹侧视觉流和下颞叶皮层，核心是受 Hopfield 网络启发的记忆模块，迭代检索代表性原型作为解释。评测采用 LPIPS、DISTS、子类对齐和 NMI 三类指标，弥补原型解释缺乏标准化评估的问题。在 CIFAR-10/100、MNIST、HAM10000 和 CUB-200 四个数据集上，EMN 平均提升视觉相似度 17.06%、子类对齐 104.57%、NMI 42.42%，同时取得 87.62% 的最高平均分类准确率。
Engram Memory Network: Brain-Inspired Prototype Explanations
Artificial intelligence systems are increasingly deployed in high-stakes decision-making. In response, prototype-based models in explainable AI have attracted growing attention for their ability to retrieve visually similar examples as faithful explanations. Although these models achieve competitive classification accuracy, they often exhibit degraded explanation quality across datasets with varying characteristics, limiting their practical applicability. We draw on insights from cognitive neuroscience, particularly the human memory system, which encodes input-relevant information into representations that support recognition. We propose an Engram Memory Network (EMN), a model that emulates the ventral visual stream and inferior temporal cortex in the human brain. Central to this model is a Hopfield-inspired memory module that iteratively retrieves representative prototypes. These memory-refined latents are used for both reconstruction and classification, encouraging the selected prototypes to capture input-relevant information across diverse datasets. To address the lack of standardized evaluation for prototype explanations, we assess three complementary aspects: perceptual similarity (LPIPS and DISTS) for perceived resemblance, subclass alignment, measuring whether prototypes match the input's fine-grained category beyond supervised labels, and information preservation (NMI), measuring how much input information is captured by selected prototypes. Experiments show that EMN improves visual similarity by 17.06%, subclass alignment by 104.57%, and NMI by 42.42% on average across CIFAR-10/100, MNIST, HAM10000, and CUB-200, while achieving the highest average classification accuracy (87.62%) among all methods. These findings underscore the effectiveness of neuroscience-inspired explanation mechanisms in advancing the interpretability and reliability of prototype-based models.