I think this is an important and maybe under-appreciated point. The dynamics of computation can significantly change with spiking neurons, especially with short synaptic time constants. I think this isn’t something we have necessarily explored much, but one simple example I was playing around with a little while ago is lateral inhibition. Lateral inhibition networks are often criticized for being slow to converge, particularly if two nodes have very similar input. For example, if you’ve got a network with three rate neurons, with inputs [1.0, 0.95, 0.5], the first neuron should eventually win out, but it could take a while, because the second and third neurons are also going to be inhibiting the first, and it will take a while to settle. If you use spiking neurons, however, the first neuron will spike first, and if you have fast inhibition, very quickly inhibit the other two neurons. If the inhibition is fast enough, the other two neurons might never spike at all, allowing the first neuron to win out very quickly.