  Simulation of spiking neural networks has been traditionally done on
high-performance supercomputers or large-scale clusters. Utilizing the parallel
nature of neural network computation algorithms, GeNN (GPU Enhanced Neural
Network) provides a simulation environment that performs on General Purpose
NVIDIA GPUs with a code generation based approach. GeNN allows the users to
design and simulate neural networks by specifying the populations of neurons at
different stages, their synapse connection densities and the model of
individual neurons. In this report we describe work on how to scale synaptic
weights based on the configuration of the user-defined network to ensure
sufficient spiking and subsequent effective learning. We also discuss
optimization strategies particular to GPU computing: sparse representation of
synapse connections and occupancy based block-size determination.
