Mathematically, over some finite interval, approximating y=sin(x) using a Taylor series, or approximating y=x**n using a Fourier series, can both require a large number of terms. But this doesn't imply that a sine/cosine function is more computationally complex than a monomial, or vice versa.
In the opposite direction, assuming we had manipulation techniques create new neurons, position them and stretch out axons arbitrarily and control synapse linking strength (which we can't now), then IMHO there could be a 1-to-1 correspondence; the same octopus neurons that McCulloch-Pitts model tried to describe could also each approximate one artificial neuron for inference. Of course, learning is a different issue, brains don't exactly do back-propagation and gradient descent.
You only need to put all your synapses at the soma and voila