There has recently been a lot of interest in converting ready-trained convolutional deep networks of artificial neurons into spiking neural networks (SNNs) for low-power inference on neuromorphic hardware. While GeNN is unlikely to compete with neuromorphic hardware in terms of energy efficiency, it is a useful and flexible platform for exploring this research area.
The first stage of this project will be to build a Python library which converts networks trained using Tensor flow into GeNN models via GeNN’s Python interface and some of the techniques discussed by Diehl et al. . Possible extensions would then include modifying GeNN to implement a more efficient convolution connector and perhaps beginning to investigate some recent attempts to train deep SNNs .
Skills required: Tensor flow, Python, C++
 Diehl, Peter U., et al. "Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing." Neural Networks (IJCNN), 2015 International Joint Conference on. IEEE, 2015.
 Zenke, Friedemann, and Surya Ganguli. "SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks." Neural computation 30.6 (2018): 1514-1541.
Mentors: Jamie Knight & Thomas Nowotny, Sussex U, UK.