The long short-term memory (LSTM) network underpins many achievements and breakthroughs especially in natural language processing fields. Essentially, it is endowed with certain memory capabilities to boost its performance. Currently, the volume and speed of big data generation are increasing exponentially, and such data require efficient models to acquire memory augmented knowledge. In this paper, we propose a memory augmented convolutional neural network (MACNN) with utilizing self-organizing maps (SOM) as the memory module. First, we depict the potential challenge about just applying solely a convolutional neural network (CNN) so as to highlight the advantage of augmenting SOM memory for better network generalization. Then, we dissert a corresponding network architecture incorporating memory to instantiate the distributed knowledge representation machanism, which tactically combines both SOM and CNN. Each component of the input vector is connected with a neuron in a two-dimensional lattice. Finally, we test the proposed network on various datasets and the experimental results reveal that MACNN can achieve competitive performance, especially for bioimages datasets. Meanwhile, we further illustrate the learned representations to interpret the SOM behavior and to comprehend the achieved results, which indicates that the proposed memory-incorporating model can exhibit the better performance.

长短期记忆（LSTM）网络支撑了许多成就和突破，尤其是在自然语言处理领域。从本质上讲，它具有一定的内存能力来提高其性能。当前，大数据生成的数量和速度呈指数级增长，此类数据需要高效的模型来获取内存增强知识。在本文中，我们提出了一种利用自组织映射（SOM）作为记忆模块的记忆增强卷积神经网络（MACNN）。首先，我们描述了仅应用卷积神经网络 (CNN) 的潜在挑战，以突出增强 SOM 内存以实现更好的网络泛化的优势。然后，我们提出了一个相应的网络架构，它结合了内存来实例化分布式知识表示机制，它在战术上结合了 SOM 和 CNN。输入向量的每个分量都与二维格子中的神经元相连。最后，我们在各种数据集上测试了所提出的网络，实验结果表明 MACNN 可以实现有竞争力的性能，尤其是对于生物图像数据集。同时，我们进一步说明了学习表示来解释 SOM 行为并理解所取得的结果，这表明所提出的记忆合并模型可以表现出更好的性能。我们在各种数据集上测试了所提出的网络，实验结果表明 MACNN 可以实现有竞争力的性能，尤其是对于生物图像数据集。同时，我们进一步说明了学习表示来解释 SOM 行为并理解所取得的结果，这表明所提出的记忆合并模型可以表现出更好的性能。我们在各种数据集上测试了所提出的网络，实验结果表明 MACNN 可以实现有竞争力的性能，尤其是对于生物图像数据集。同时，我们进一步说明了学习表示来解释 SOM 行为并理解所取得的结果，这表明所提出的记忆合并模型可以表现出更好的性能。