A more interesting angle would be "how complex is the genetic description of a neuron"? Or "How many degrees of freedom are there in the genetic description of a neuron"?
It might give us an idea of how many types of models we would need to correspond to all genetically reasonable types of neurons.
(my interpretation of the comment) For example, if there are like 8 free variables on neuron formation from the genetic blueprints, and then we find they produce reasonably different neuron types about 16 times over the range of that variable. We could end up with 4 billion possible models that we would need to simulate all possible neurons; then I guess you'd want to reduce those by saying that these two were like these other two and starting condensing them down; I'm not sure where this would go, but maybe we would find something cool or find a new insight by figuring out all possibilities
The difference between being able to describe a neuron's behavior and being able to simulate that behavior is the difference between being able to say "ten trillion trillion PN junctions" and being able to build a working computer that fits that description.
This paper has found that you need 1000 artificial neurons to simulate a real one. This doesn't say much about the actual complexity of a neuron though, e.g. I'm sure you need many neurons to simulate a Fourier transform, even though the Fourier transform only has a few mathematical terms (in other words, the true complexity of a FT is much lower). What would tell us about its actual complexity though is if we knew how many "variables" the formula describing a single neuron has, which should be approximated by how many degrees of freedom in nature does its genetic description have.
i wonder if its the case that neurons are something of a Turing Tarpit doing something like implementing a Turing Machine but only using a limited number of operators other basic operation having to be implemented from the ones present (ie making a on XOR out of NAND gates for example) just highly optimized for a particular use-case.
So, we know that human-like general intelligence is possible because we display it right? We're pretty sure it has something to do with that soft stuff inside our brain-baskets, and how that soft stuff is interconnected. We've found computational primitives that we call neurons, so the premise goes, if we can understand and copy how it works we can engineer intelligence. Right? So going to genetics, while interesting and worthy, isn't really going up a level of 'abstraction' because we're trying to get a systems level overview here.
It is a process somewhat like studying a CPU by shocking prongs and occasionally slicing it real thin to see inside it.
That isn't really pertinent or I'd argue interesting in the context of this article, at all!
It might be helpful to know that (some | many | most) people think of biological neurons as being sort of poor-quality artificial ones, or at least that there is a direct correspondence between an organic neuron and an artificial one. This paper is making the argument that a given biological neuron is more like a network of artificial neurons, which is interesting and important because a lot of research into intelligence right now is going into making 'biologically plausible' models and testing those. If their results are essentially that each neuron has the sophistication of a network then say a future model of a cortical 'cell' (a cluster of neurons we think have a purpose as a group) should take this into account.
Genetics are interesting but neurologically we're still at the phase of knowledge where we are making up wild-ass conjectures and shooting holes in them for want of anything better to be doing.
