Researchers have long been captivated by the intricate workings of the human brain, an enduring enigma. Extensive efforts have been devoted to unraveling its complexities, with disciplines like psychology employing experimentation and analysis to scrutinize and formulate models of brain function. Comprising billions of interconnected neurons, the human brain has inspired experts in deep learning to construct artificial neural networks capable of tasks akin to human brain functions, such as pattern and speech recognition. Despite substantial progress in artificial intelligence, advancements in memory storage capabilities have been relatively constrained. This study aims to investigate mechanisms for simulating long-term declarative episodic memory, reminiscent of human cognition, using one-shot deep-learning neural networks. The proposed deep learning architecture extends to Rosenblatt's C-system memory model, and experiments were conducted to assess the effectiveness of various adaptations of the C-system storage mechanism. The fashion MNIST dataset is used in the experiments, and the results indicate that these models exhibit proficient recall abilities, even when faced with a large number of input images. Furthermore, the study delves into emulating the forgetting process of the human brain. The experiment demonstrates that as the units in the C-system increase, the corresponding results also increase. Specifically, when employing 40,000 units, the system maintains an accuracy exceeding 92% for the sequence of images.

中文翻译：

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一种通过一次性学习建立长期陈述性情景记忆的计算深度学习方法

长期以来，研究人员一直对人类大脑的复杂运作机制着迷，这是一个持久的谜。为了揭示其复杂性，人们付出了广泛的努力，心理学等学科利用实验和分析来仔细检查和制定大脑功能模型。人脑由数十亿个相互连接的神经元组成，启发了深度学习专家构建能够执行类似于人脑功能的任务的人工神经网络，例如模式和语音识别。尽管人工智能取得了长足的进步，但内存存储能力的进步却相对受到限制。这项研究旨在研究使用一次性深度学习神经网络模拟长期陈述性情景记忆的机制，这让人想起人类认知。所提出的深度学习架构扩展到 Rosenblatt 的 C 系统内存模型，并进行了实验来评估 C 系统存储机制的各种适应的有效性。实验中使用了时尚 MNIST 数据集，结果表明，即使面对大量输入图像，这些模型也表现出熟练的回忆能力。此外，该研究还深入模拟人脑的遗忘过程。实验表明，随着C系统中单元的增加，相应的结果也会增加。具体来说，当使用 40,000 个单元时，系统对图像序列的准确度保持在 92% 以上。