It's tricky to be both reasonably biologically accurate, and yet computationally efficient. Some models do exist to fit this niche. One I'm familiar with is Izh

It's tricky to be both reasonably biologically accurate, and yet computationally efficient. Some models do exist to fit this niche. One I'm familiar with is Izhikevich (2003), a model with four morphology parameters and two state variables and corresponding ordinary differential equations. It can simulate the spiking behaviour of several different kinds of neurons. https://www.izhikevich.org/publications/spikes.htm https://www.izhikevich.org/publications/spikes.htm I've written a SIMD-based simulator that can integrate 500k of those neurons in real-time on a standard laptop. That's a bit of a limit case -- it doesn't yet model action potentials or learning, which would slow it down quite a bit. The details here matter a lot: a GPU version I experimented with could do about 1M, whereas an earlier version of this work that was a straight-up CPU-based solution (_not_ SIMD) but _with_ action potentials and learning could only model 1k.