Memory modules such as differentiable neural computer  and end-to-end memory networks  are often used in deep learning to store medium and long term memories. These contribute to the success of architectures that can solve extremely difficult tasks such as natural language question answering and multi-player capture the flag .
Recent alternatives have emerged that are inspired by the brain while being simpler and having the potential to be more efficient — for example using Hebbian plasticity .
This Masters thesis will explore further alternatives to memory modules that use forms of memory and plasticity that have further advantages in terms of memory capacity and efficiency. These could be based on, for example, , . Exploring connections to biological forms of plasticity will be encouraged. The modules will be implemented and evaluated for supervised learning and/or reinforcement learning tasks. Such modules are especially of interest in reinforcement learning to emulate episodic memory.
Knowledge of Python and deep learning frameworks such as Tensorflow/Pytorch/JAX.
 Jaderberg, Max, et al. ‘Human-Level Performance in 3D Multiplayer Games with Population-Based Reinforcement Learning’. Science, vol. 364, no. 6443, May 2019, pp. 859–65. doi:10.1126/science.aau6249.