Event-based neural network models are biologically inspired  and have the ability to scale well  while being extremely energy efficient on neuromorphic hardware .
Create a high-performance implementation of event-based models based on . This can be using one of the following
- Rust or Elixir
- As an extension of GeNN
- Using MPI in C++
- For the Graphcore AI chip.
- Good Knowledge of Rust/Elixir/C++/CUDA depending on the choice of implementation.
- Knowledge of concurrent programming concepts.
- Basic Knowledge in deep learning.
 Pfeiffer, Michael, and Thomas Pfeil. "Deep learning with spiking neurons: opportunities and challenges." Frontiers in neuroscience
(2018): 774. (link
 Jordan, J., Ippen, T., Helias, M., Kitayama, I., Sato, M., Igarashi, J., Diesmann, M. and Kunkel, S., 2018. Extremely scalable spiking neuronal network simulation code: from laptops to exascale computers. Frontiers in neuroinformatics
, p.2. (link
, a GPU-enhanced Neuronal Network simulation
 Christensen, Dennis Valbjørn, et al. "2022 roadmap on neuromorphic computing and engineering." Neuromorphic Computing and Engineering (2022).
 Subramoney, A., Nazeer, K.K., Schöne, M., Mayr, C., Kappel, D., 2022. EGRU: Event-based GRU for activity-sparse inference and learning. https://doi.org/10.48550/arXiv.2206.06178