Mistaken idea: genes are like a program in code that defines what biology does.
More accurate idea: genes are like the NVRAM of a running program that has run continuously with in-place updates for 4 billion years.
They also represent the NVRAM of the compiler that builds both the program and the compiler itself :)
It is a process somewhat like studying a CPU by shocking prongs and occasionally slicing it real thin to see inside it.
(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
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.