MemoryNetze

MemoryNetze is a term used in AI and cognitive modeling to describe a family of neural network architectures that incorporate external differentiable memory into the computation graph to store and retrieve information across time. These networks aim to extend the learning and reasoning capabilities of standard neural models by allowing explicit memory of past observations and actions.

These systems typically consist of a controller, which can be an LSTM, GRU, or Transformer, and an external memory matrix M. Read and write heads perform differentiable operations; reads retrieve content-based or location-based information; writes update memory with new observations. The model learns how to address memory through attention mechanisms and gating, enabling selective access to stored information during inference.

MemoryNetze encompass several concrete instantiations such as memory networks (MemNN), neural Turing machines, and differentiable neural computers. Training relies on backpropagation through time; some designs use reinforcement signals to guide memory addressing. Limitations include memory capacity, training stability, and interpretability, though research seeks to improve modularity and compositional reasoning.

Applications include question answering, reasoning over documents, long-horizon planning in robotics, dialogue systems, and cognitive modeling of human memory processes. These networks are evaluated on tasks that require retrieval of information from past interactions or structured episodic data, often emphasizing the integration of memory with reasoning.

See also: Neural Turing Machines, Differentiable Neural Computer, Memory networks, external memory, attention.