A key feature of intelligent behaviour is the ability to learn abstract strategies that scale and transfer to unfamiliar problems. An abstract strategy solves every sample from a problem class, no matter its representation or complexity—similar to algorithms in computer science. Neural networks are powerful models for processing sensory data, discovering hidden patterns and learning complex functions, but they struggle to learn such iterative, sequential or hierarchical algorithmic strategies. Extending neural networks with external memories has increased their capacities to learn such strategies, but they are still prone to data variations, struggle to learn scalable and transferable solutions, and require massive training data. We present the neural Harvard computer, a memory-augmented network-based architecture that employs abstraction by decoupling algorithmic operations from data manipulations, realized by splitting the information flow and separated modules. This abstraction mechanism and evolutionary training enable the learning of robust and scalable algorithmic solutions. On a diverse set of 11 algorithms with varying complexities, we show that the neural Harvard computer reliably learns algorithmic solutions with strong generalization and abstraction, achieves perfect generalization and scaling to arbitrary task configurations and complexities far beyond seen during training, and independence of the data representation and the task domain.

智能行为的关键特征是能够学习可扩展并转移到陌生问题的抽象策略的能力。抽象策略可以解决问题类别中的每个样本，无论其表示形式或复杂程度如何（类似于计算机科学中的算法）。神经网络是用于处理感官数据，发现隐藏模式和学习复杂功能的强大模型，但它们难以学习此类迭代，顺序或分层算法策略。用外部存储器扩展神经网络提高了其学习此类策略的能力，但它们仍易于发生数据变化，难以学习可扩展和可转移的解决方案，并且需要大量的训练数据。我们介绍了神经哈佛计算机，一种基于内存的基于网络的体系结构，该体系结构通过将算法操作与数据操作解耦而采用抽象，该抽象是通过拆分信息流和分离的模块来实现的。这种抽象机制和进化训练使学习健壮和可扩展的算法解决方案成为可能。在具有复杂性变化的11种算法的多样化集合上，我们表明神经哈佛计算机能够可靠地学习具有强大泛化和抽象能力的算法解决方案，能够实现完美的泛化和扩展到任意任务配置和复杂性，而这些复杂性和复杂性远远超出了训练期间所见的范围以及数据的独立性表示形式和任务域。这种抽象机制和进化训练使学习健壮和可扩展的算法解决方案成为可能。在具有复杂性变化的11种算法的多样化集合上，我们表明神经哈佛计算机能够可靠地学习具有强大泛化和抽象能力的算法解决方案，能够实现完美的泛化和扩展到任意任务配置和复杂性，而这些复杂性和复杂性远远超出了训练期间所见的范围以及数据的独立性表示形式和任务域。这种抽象机制和进化训练使学习健壮和可扩展的算法解决方案成为可能。在具有复杂性变化的11种算法的多样化集合上，我们表明神经哈佛计算机能够可靠地学习具有强大泛化和抽象能力的算法解决方案，能够实现完美的泛化和扩展到任意任务配置和复杂性，而这些复杂性和复杂性远远超出了训练期间所见的范围以及数据的独立性表示形式和任务域。