The paper introduces the Neural Turing Machine (NTM), a neural network architecture that integrates external memory resources and attentional processes, extending the capabilities of traditional neural networks. The NTM is designed to perform algorithmic tasks by interacting with a memory matrix through selective read and write operations, similar to a Turing Machine or Von Neumann architecture but with end-to-end differentiability, allowing efficient training via gradient descent. The authors demonstrate that NTMs can learn simple algorithms such as copying, sorting, and associative recall from input and output examples. The NTM architecture consists of two main components: a neural network controller and a memory bank. The controller interacts with the external environment and the memory matrix, while the memory bank stores and retrieves information. The key innovation is the use of "blurry" read and write operations, which interact with parts of the memory matrix rather than specific elements, allowing for sparse and selective memory access. This is achieved through an attentional "focus" mechanism that determines the degree of interaction with different parts of the memory. The paper also discusses the foundational research on working memory, cognitive science, and recurrent neural networks, highlighting the importance of external memory and dynamic state in computational models. The NTM architecture is compared to LSTM networks and standard LSTM networks in experiments on tasks such as copying, repeat copying, associative recall, dynamic N-Grams, and priority sort. The results show that NTMs outperform LSTM networks in terms of learning speed, generalization, and ability to solve complex algorithmic tasks. In conclusion, the NTM is a powerful tool for learning and executing simple algorithms, demonstrating the potential for further development in areas such as cognitive science and artificial intelligence.The paper introduces the Neural Turing Machine (NTM), a neural network architecture that integrates external memory resources and attentional processes, extending the capabilities of traditional neural networks. The NTM is designed to perform algorithmic tasks by interacting with a memory matrix through selective read and write operations, similar to a Turing Machine or Von Neumann architecture but with end-to-end differentiability, allowing efficient training via gradient descent. The authors demonstrate that NTMs can learn simple algorithms such as copying, sorting, and associative recall from input and output examples. The NTM architecture consists of two main components: a neural network controller and a memory bank. The controller interacts with the external environment and the memory matrix, while the memory bank stores and retrieves information. The key innovation is the use of "blurry" read and write operations, which interact with parts of the memory matrix rather than specific elements, allowing for sparse and selective memory access. This is achieved through an attentional "focus" mechanism that determines the degree of interaction with different parts of the memory. The paper also discusses the foundational research on working memory, cognitive science, and recurrent neural networks, highlighting the importance of external memory and dynamic state in computational models. The NTM architecture is compared to LSTM networks and standard LSTM networks in experiments on tasks such as copying, repeat copying, associative recall, dynamic N-Grams, and priority sort. The results show that NTMs outperform LSTM networks in terms of learning speed, generalization, and ability to solve complex algorithmic tasks. In conclusion, the NTM is a powerful tool for learning and executing simple algorithms, demonstrating the potential for further development in areas such as cognitive science and artificial intelligence.