The paper introduces the Differentiable Neural Computer (DNC), a neural network architecture that integrates external memory access with neural computation. DNCs are designed to address the limitations of traditional neural networks in handling complex data structures and long-term memory tasks. The DNC architecture allows the network to read from and write to external memory, enabling it to store and manipulate large data structures. The system uses sparse memory access to minimize interference and enable long-term storage, and it can be trained using gradient descent to learn how to operate and organize the memory in a goal-directed manner. The authors demonstrate DNC's capabilities through various experiments. They show that DNC can perform question-answering tasks involving graphs, such as finding shortest paths and inferring missing links. Additionally, DNC can learn complex symbolic instructions in a game environment through reinforcement learning, indicating its potential for tasks requiring pattern recognition and symbol manipulation. The DNC architecture is compared to other neural network models, including the Neural Turing Machine (NTM) and Long Short-Term Memory (LSTM). DNC outperforms these models in tasks like the bAbI dataset, which involves synthetic question-answering, and graph-based reasoning tasks. The DNC's ability to handle structured data and learn from symbolic instructions suggests that it is a promising model for cognitive tasks that require both pattern recognition and symbol manipulation.The paper introduces the Differentiable Neural Computer (DNC), a neural network architecture that integrates external memory access with neural computation. DNCs are designed to address the limitations of traditional neural networks in handling complex data structures and long-term memory tasks. The DNC architecture allows the network to read from and write to external memory, enabling it to store and manipulate large data structures. The system uses sparse memory access to minimize interference and enable long-term storage, and it can be trained using gradient descent to learn how to operate and organize the memory in a goal-directed manner. The authors demonstrate DNC's capabilities through various experiments. They show that DNC can perform question-answering tasks involving graphs, such as finding shortest paths and inferring missing links. Additionally, DNC can learn complex symbolic instructions in a game environment through reinforcement learning, indicating its potential for tasks requiring pattern recognition and symbol manipulation. The DNC architecture is compared to other neural network models, including the Neural Turing Machine (NTM) and Long Short-Term Memory (LSTM). DNC outperforms these models in tasks like the bAbI dataset, which involves synthetic question-answering, and graph-based reasoning tasks. The DNC's ability to handle structured data and learn from symbolic instructions suggests that it is a promising model for cognitive tasks that require both pattern recognition and symbol manipulation.