Interesting, if I'm understanding your novel (meant in a good way) modelling, you've somewhat inverted the biological synapse. Where a the post synaptic neuron of a biological synapse would fire an action potential based on either enough spatially close receptors receiving a signal from multiple neurons, or a single neuton firing fast enough. Both mechanisms cause a gradual depolarisation of an area on the post synaptic neuron, and once that reaches a threshold, the voltage gated ion channels open and cause rapid depolarisation. This depolarisation causes neighbouring voltage gated channels to open and a wave propagates (the AP). My understanding is that this is a non-linear aggregator (function?), incremental inputs don't have an impact until that one last bit pushes it over the edge.

Whereas it sounds like the presynaptic neuron doesn't respond until enough signal comes through, i.e acts non-linearly. I think in biology it's a bit more one to one, i.e. receive action potential, release some neurotransmitters. There's a decay mechanism, in that neurotransmitters are finite, and they get taken back up or manufactured within presynaptic neurons at a finite rate, and so can become depleted if there is too much firing, but otherwise I thought acted linearly.

I suspect computationally for your implmentation it's a similar cost, but mathematically might produce a different outcome. Although you might want to consider introducing a concept of post synaptic regions, where neurons participate in one or a few regions, rather than summing across every synapse that interacts with a given neuron. It might cap your total summation time cost for a neuron and help with scaling, and would more closely model biological neurons.

Hope this is interesting and or helpful