  Maintaining the ability to fire sparsely is crucial for information encoding
in neural networks. Additionally, spiking homeostasis is vital for spiking
neural networks with changing numbers of weights and neurons. We discuss a
range of network stabilisation approaches, inspired by homeostatic synaptic
plasticity mechanisms reported in the brain. These include weight scaling, and
weight change as a function of the network's spiking activity. We tested
normalisation of the sum of weights for all neurons, and by neuron type. We
examined how this approach affects firing rate and performance on clustering of
time-series data in the form of moving geometric shapes. We found that neuron
type-specific normalisation is a promising approach for preventing weight drift
in spiking neural networks, thus enabling longer training cycles. It can be
adapted for networks with architectural plasticity.
